RU2571405C1 - Method for preliminary conversion of structured data array - Google Patents

Abstract

FIELD: information technologies.

SUBSTANCE: in the method of conversion of a structured data array they generate initial structure of data of the initial data array. Context terms are identified in initial elements, and the data base of context terms is formed. Using information contained in the generated data base, in the initial data structure they identify structural-complex language structures, containing context terms, structural-complex language structures, not containing context terms, structural-simple language structures, containing context terms, and structural-simple language structures, not containing context terms. Conversions of language structures are performed, and the final data structure of the structured initial data array is formed.

EFFECT: increased accuracy and speed of navigation by elements of data array due to provision of preliminary conversion of a structured data array and further generation of logically, grammatically and orthographically correct data structure.

19 cl, 19 dwg, 6 tbl

Description

The group of inventions relates to solutions in the field of processing data arrays, in particular, to solutions in the field of processing structured data arrays containing natural language text, and can be used for preliminary conversion of a structured data array containing natural language text, for the convenience of its subsequent processing.

BACKGROUND

From patent EA 002016 B1, G06F 17/30, 10.22.2001 (MATVEEV LEV LAZAREVICH AND OTHERS) a method is known for searching for fragments similar in text and / or semantic content in electronic documents stored on data storage devices, which consists in indexing each stored in document archive, dividing the mentioned documents into fragments and forming topics from one or more fragments, determining search parameters, conducting a search, ranking the list of document fragments obtained as a result of a search, moreover, as parameters claims are determined by the set of unique blocks of information included in the selected fragment of the document, and are expanded by preliminary processing of each of the mentioned unique blocks of information, where a unique block of information means the block of information that occurs one or more times in the selected fragment of the document, where they are used as preliminary processing the operation of obtaining at least one unique block of information, one or more blocks of information associated with a unique block of information tion by a given ratio.

From patent RU 2476927 C2, G06F 17/30, 02.27.2013 (ANSHUKOV SERGEY ALEXANDROVICH AND OTHERS), a method for positioning texts in the knowledge space is known, which consists in distinguishing elements from the input data that correspond to patterns that are included in taxa forming taxonomies combined in ontology; significant taxa are determined that are weighted based on the conditions assigned to the patterns; make up a set of weighted vectors that position the input document in the knowledge space, characterized in that it uses a lot of ontologies for positioning, and also in that when compiling sets of vectors, only those elements that correspond to patterns included in one tax or in taxa are considered, having common parent taxa.

From patent RU 2210809 C2, G06F 17/28, 08/20/2003 (OPEN JOINT-STOCK COMPANY “MOSCOW TELECOMMUNICATION CORPORATION”), a method is known for automatically converting the source text into a set of interconnected objects based on a settings table containing knowledge about the structure of the system under study in the form of the set forming it classes that include a specific set of attributes (including relationships and relationships between objects of given classes) and rules for recognizing an attribute in the text established for each attribute. It is possible to determine the format of the source text and automatically translate its fragments during the formation of objects.

From patent RU 2292078 C1, G06F 17/30, 20.01.2007 (MEDIA LINGUA CLOSED JOINT-STOCK COMPANY), a method for searching, marking and displaying information is known, including entering the desired data objects of the source electronic documents to be searched on information networks from a network subscriber terminal, performing the function of the request source of the desired data objects, comparing the desired data objects of the source electronic documents with the control data objects of the associated information in the information network, and if the desired objects coincide with the control transformation of data objects of source electronic documents by marking data objects with hyperlinks, visualization of electronic documents with hyperlinks at the subscriber terminal of the network and calling to the subscriber terminal of the data network of associated information of the information network, characterized in that at least two are created before marking data areas, at least one of which is a resident area for the request source of the desired data objects and provides access to data objects of primary hyperlinks containing additional parameters for addressing at least one other region, and at least one other region is non-resident for the query source of the desired data objects and provides binding to data objects of secondary hyperlinks for addressing at least one data resource of associated information for access to it from subscriber terminals of at least one resident area that is the source of the primary hyperlink, while in the res In this area, an array of control data objects is created with primary hyperlinks corresponding to each specified object as related data, and in a non-resident area, an array of control data objects of associated information is created with at least one secondary hyperlink corresponding to each specified object as information associated information network.

From patent RU 2386166 C2, G06F 17/30, 04/10/2010 (OPEN JOINT STOCK COMPANY TAGANROG AVIATION SCIENTIFIC AND TECHNICAL COMPLEX NAMED AFTER G.M. BERIEV), a method for generating a knowledge base is known which is formed in the form of a three-dimensional information space in which data about a document or its parts are defined in a cluster or clusters formed by single segments (orts) of characteristic features. The full identification number of the document is formed from the codes orth component characteristic features and the identification number of the document. Each cluster is analyzed for the completeness of the definition of the limited scope of activity contained in the documents contained in the cluster. The result of the analysis is recorded in the same cluster. Search and analysis of data is carried out both by means of the formation and processing of the request, and in the opposite direction by preparing the database for the expected user. The system also provides tools for working with the database, for searching, controlling and analyzing information, documents, areas of activity, for creating and updating documents by system administrators, experts and users in accordance with access rights.

From patent RU 2253893 C2, G06F 17/27, 06/10/2005 (CHERNIKOV BORIS VASILIEVICH) a method for automated lexicological synthesis of documents is known, including the creation and preservation of a unified form of a document, classification of the contents of a document by highlighting unified constant information and variable information, storing constant information in databases, entering constant information into a unified form of the document and introducing variable information into the document, in which the variable un fictitious information associated with stable formulations, variable input information representing concretizing information, and variable non-standardized information containing free formulations, the variable unified information being distinguished by forming a set of support words that uniquely identify specific formulations in the document and constitute the lexicological skeleton of the document, and save the document in excess with respect to a single copy in a machine database a, the lexicological tree of the document is formed by determining the interdependence of the individual supporting words and then the information management loop of the document is formed by establishing a method for introducing formulations of the variable of unified and unified information depending on the nature of the relationship between the supporting word and the document fragment.

From WO 2013043160 A1, G06F 17/21, 03/28/2013 (HEWLETT PACKARD DEVELOPMENT CO ET AL.), A method for processing a text data array is known, which consists in constructing a graph representing a micromodel of entities that form the body of the processed document. The division of such text into nodes of the graph, with each node referring to its own particulars of the selected fragment from the text, and the said nodes of the graph are interconnected by relations, similar to the connection of fragments of text corresponding to the said nodes. Subsequently, the constructed nodes of the graph are ranked to determine the relevant data regarding the user's request.

From the application WO 2001001289 A1, G06F 17/27, 01/04/2001 (INV MACHINE CORP INC) a method is known which consists in semantic processing of data presented in a natural language, the method including entering and storing user conditions, which are then used for searching in data arrays containing natural language data, text representations containing information relevant to user input, formatting the aforementioned representations, extracting subject-action-object relationships from formatted text representations CT (LMS) and their storage in a remote storage location, for example, a database, restructuring the identified LMS into a normalized form, assigning parts of the LMS, such as an action-object (BO), as the name of the folders that contain parts of the LMS and the destination with the indicated folders of one or more identical associated parts of the subject (S1, S2 ... Sn), which are associated with the corresponding TO parts. The method also allows you to associate sentences containing the corresponding elements of the subjects S1, S2 ... Sn, and select relevant SDOs in them with their subsequent labeling against the background of a common data array.

From the patent US 8229730 B2, G06F 17/30, 24.07.2012 (MICROSOFT CORP ET AL.) A method is known for searching data at the request of a user presented in natural language, the method being that the text data array is parsed with assignment of grammatical roles terms and their subsequent indexation, which are in semantic connection with the terms of the search query, and the mentioned roles contain dominant and secondary roles that are identified in the analysis of the user query. This method allows you to determine the relevant parts of the document containing terms with roles that match the roles of the text of the user’s request.

From the application EP 2400400 A1, G06F 17/27, 12.28.2011 (INBENTA PROFESSIONAL SERVICES SL), a method for semantic search for relevant information is known, which consists in the fact that using lexical functions and a criterion, the values of the text in the data array presented in natural language are formed phrases or expressions obtained from the content database, and select an answer that has an impending indicator of semantic correspondence, and the method consists in transforming the content and requesting independent words or groups of words with the tokens assigned to them that prevail azovyvayutsya in semantic representations, thereby applying the rules of the meaning of the text by means of the criterion of lexical functions, each of these semantic representations consists of a lemma and a semantic category.

From the application WO 2010105216 A2, G06F 17/20, 09/16/2010 (INVENTION MACHINE CORP), a method for marking text data of a document is known, which consists in linguistically analyzing a document, comparing the document after its analysis with a template of the required semantic relations between objects, forming semantically marked-up text using semantic links based on linguistic analysis of the text and comparison with the template of semantic links, with semantic labels associated with words or phrases of text sentences, and ide components of certain semantic relations are identified, with the subsequent storage of semantically marked-up text in the database for the subsequent search for relevant information on the obtained data structure.

From the application EP 2105847 A1, G06F 17/30, 09/30/2009 (ALCATEL LUCENT), a method for automatically generating an ontology is known, which consists in accepting a term for which it is necessary to form an ontology, determining the meaning of the term using a dictionary, and extracting suitable definitions for the said term, determine the meaning of each of the extracted definitions using the aforementioned dictionary, build for each of the defined meanings of the term and each suitable term for the term of the initial ontol creation request ogy, at least one logical paragraph describing the relationship between said pair of suitable terms, said logic input points define the ontology term.

All of the above solutions do not allow the formation of a semantically and logically correctly structured data array from the original data array containing text presented in natural language by splitting the said array into logical sections, which are subjected to semantic decomposition of the structures of the sections themselves and the elements included in the sections mentioned, their subsequent spelling and grammatical analysis, and the subsequent assessment of their interconnectedness in the original data array.

The closest analogue (prototype) of the claimed solution is a method for automated processing of natural language text by means of its semantic indexing, described in patent RU 2399959 C2, G09B 19/00, 09/20/2010 (CLOSED JOINT STOCK COMPANY "AVIKOMP SERVISES"). The known method is a method in which text is segmented in electronic form into elementary units, identifies stable phrases, form sentences, identifies semantically significant objects and semantically significant relationships between them, form many triads for each semantically significant relationship, in which a single triad of the first type corresponds to a relationship established by a semantically significant relationship between two semantically significant objects, each of the triads of the second type of co corresponds to the value of a specific attribute of one of these semantically significant objects, each of the triads of the third type corresponds to the value of a specific attribute of the semantically significant relation, index all related semantically meaningful relations separately from the set of triads, store the generated triads and obtained indices in the database together with a link to the source text from which these triads are formed.

The disadvantage of this method is that when forming the mentioned triads, the text is segmented directly into elementary units, i.e. words, and not into logical sections, while this method does not provide for the formation of an intermediate structure of the original text array for its subsequent grammatical and spelling analysis and does not provide the formation of a final logical, grammatical and spelling correct data structure suitable for quick and convenient navigation through structure elements .

SUMMARY OF THE INVENTION

Based on this, the task to which the claimed invention is directed is to provide such preliminary processing of a structured data array containing natural language text that would allow further transformations to be carried out and generate a logical, grammatical and spelling correct resulting structure containing logical constructions array elements and provides quick and convenient navigation through array elements.

The technical result is the preliminary conversion of a structured data array suitable for the further formation of a logical, grammatically and spelling-correct data structure.

The claimed technical result is achieved due to the fact that they perform a method of converting a structured source data array containing at least natural language text, said method comprising at least the steps of:

A) form the initial data structure of a structured source data array containing the source elements of the original data structure, and the said source elements are:

structurally complex linguistic constructions, which are structurally complex linguistic constructions containing contextual terms and structurally complex linguistic constructions that do not contain contextual terms; and

structurally simple language constructs, which are structurally simple language constructs containing contextual terms, and structurally simple language constructs that do not contain contextual terms;

B) identify context terms in said source elements and form a database of context terms containing said identified context terms;

C) using the information contained in said generated database of contextual terms, structurally complex linguistic constructs containing contextual terms, structurally complex linguistic constructs not containing contextual terms, structurally simple linguistic constructs containing contextual terms are identified in said initial data structure , and structurally simple language constructs that do not contain contextual terms;

D) carry out the conversion of the first type on the above-mentioned structurally complex language constructs that do not contain contextual terms, to obtain grammatically and spelling-correct structurally simple language constructs that do not contain contextual terms;

E) carry out the conversion of the second type over the mentioned structurally simple language constructs containing contextual terms, to obtain grammatically and spelling correct structurally simple language constructs that do not contain contextual terms;

E) carry out the conversion of the third type over the above-mentioned structurally complex language constructs containing contextual terms, to obtain grammatically and spelling-correct structurally simple language constructs that do not contain contextual terms;

G) form the final data structure of the structured initial data array containing the elements of the final data structure, and the elements of the final data structure are structurally simple language constructs that do not contain contextual terms and obtained grammatically and spelling structurally correct due to transformations of the first, second and third types -simple language constructs that do not contain contextual terms.

Embodiments of the present invention relate to a method, apparatus, system, and computer-readable medium for efficiently converting a structured data array containing at least natural language text.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments of the present invention are described below in detail with reference to the accompanying drawings, which are incorporated herein by reference, and in which:

In FIG. 1 depicts a general flowchart of the inventive method for converting a structured source data array containing at least natural language text in accordance with a first embodiment of the present invention.

In FIG. 2 depicts a general flowchart for the steps of the claimed method for converting a structured source data array containing at least natural language text in accordance with a third embodiment of the present invention.

In FIG. 3 shows a general diagram of the step of generating the first data structure.

In FIG. 4 shows the general structure of the original data structure from which the first data structure is formed.

In FIG. 5 shows a general diagram of the stage of forming a database of logical connections of logical partitions.

In FIG. 6 shows the general principle of forming a database of logical connections of logical partitions.

In FIG. 7 shows a general diagram of a step for generating a second data structure.

In FIG. 8 shows the general structure of a second data structure.

In FIG. 9 shows the general scheme of the stage of formation of the database of semantic parts.

In FIG. 10 depicts the general principle of forming a database of semantic parts.

In FIG. 11 shows a general diagram of the stage of formation of grammatically and spelling-correct semantic parts.

In FIG. 12 shows a general diagram of a second data structure obtained after the step of forming grammatically and spelling-correct semantic parts.

In FIG. 13 shows a general diagram of the step of generating the resulting data structure.

In FIG. 14 shows a general diagram of the resulting data structure.

In FIG. 15 shows the general structure of the elements of the resulting data structure.

In FIG. 16 depicts a general flowchart of the steps of the claimed method of forming a map of links of components of grammatically and spelling-correct semantic parts of logical sections of logical structures of a transformed structured source data array.

In FIG. 17-18 depict a general diagram of the steps of the claimed method for searching for relevant information in a transformed structured source data array containing at least logical constructions containing grammatically and spelling-correct semantic parts of logical sections of logical constructions.

In FIG. 19 shows a general diagram of a system containing a device for implementing the claimed methods for converting a structured source data array, methods for generating a map of the components of a transformed structured source data array, and methods for finding relevant information in a converted structured source data array.

MODES FOR CARRYING OUT THE INVENTION

The following are embodiments of the present invention, revealing examples of its implementation in private versions. However, the description itself is not intended to limit the scope of the rights granted by this patent. Rather, it should be assumed that the claimed invention can also be carried out in other ways in such a way that it will include different steps or combinations of steps similar to the steps described herein, in combination with other existing and future technologies.

The claimed method will be considered on the example of processing a structured data array containing natural language text, which is, without limitation, regulatory legal acts (NPA). It should be obvious to a person skilled in the art that despite the fact that in this particular implementation example of the present invention, the conversion of NLA is carried out, such a conversion method can be applied to any structured data array similar to NLA.

NLA is a document characterized by the following features:

1) NLAs are of a law-making nature: in them, the rules of law are either established, changed or canceled. Normative legal acts are carriers of legal norms;

2) the regulatory legal act contains legal instruments with the help of which the legal regulatory influence is implemented.

3) NPA is published only within the competence of the law-making body;

4) NPA is clothed in documentary form and has the following details: type of regulatory act, its name, authority that adopted it, date, place of adoption of the act, number;

5) NPA is not a chaotic set of provisions (proposals), but has a certain structure;

6) The NLA must be consistent with the constitution or another higher NLA, which has greater legal force.

7) NPA must be brought to the attention of citizens and organizations, i.e. publication, and only after that, the state has the right to demand its rigorous execution, based on the presumption of knowledge of the law, and impose sanctions for its failure.

It should be noted that the term “structured data array” within the framework of the claimed invention can be considered not only a set of legal acts, but also a separate independent legal acts, which is, for example: the Constitution, law, decree, regulation, etc. A separate LA can, for example, consist of parts, chapters, sections, articles. At the same time, the instrument of the legal regulatory impact of the legal acts is the legal rule, denounced in the structure of the regulatory requirement, which in turn is an element (part) of the rule of law (legal norm).

A structured source data array containing at least natural language text is, in particular, a legal entity, which is a text consisting of structurally simple and structurally complex language constructs.

Under the language construct for the purposes of the present invention refers to a syntactically related combination of words, verbal construction.

For the purposes of the present invention, a structurally simple linguistic construction is understood to mean complete text fragments — grammatically organized word combinations, i.e. offers. Moreover, each sentence is necessarily characterized by semantic completeness.

For the purposes of the present invention, structurally complex linguistic construction is understood as constructions of the “listing category” type.

An example of a language construct related to “listing headings” is the following language construct:

“The consumer, if defects are found in the product, if they have not been agreed upon by the seller, have the right to choose:

- require replacement of goods of the same brand (of the same model and (or) article);

- require replacement for the same product of another brand (model, article) with the corresponding recalculation of the purchase price;

- demand a commensurate reduction in the purchase price;

- require immediate gratuitous elimination of defects in the goods or reimbursement of expenses for their correction by the consumer or a third party;

- refuse to fulfill the contract of sale and demand the return of the amount paid for the goods. ”.

The main goal of the preliminary transformation of a structured source data array is the formation of a structured data array consisting only of sentences. This goal is realized by converting structurally complex linguistic constructions into structurally simple linguistic constructions - sentences.

Another purpose of the preliminary conversion is to clarify contextual terms and simplify sentences containing contextual terms.

For the purposes of the present invention, contextual terms are understood to mean the conditional naming of such terms that either do not have an independent semantic meaning outside the context, or have many semantic meanings in the context.

Moreover, for the purposes of the present invention, the context refers to a relatively finished meaningful passage of text within which the meaning and meaning of a single word, phrase or combination of phrases, as well as a group of at least two sentences, in two different passages are most accurately and clearly identified. text, provided that in one of the sentences there is a link to another sentence.

In a first embodiment of the present invention, there is provided a method for converting a structured source data array containing at least natural language text, said method comprising at least the steps of:

A) form the initial data structure of a structured source data array containing the source elements of the original data structure, and the said source elements are:

structurally complex linguistic constructions, which are structurally complex linguistic constructions containing contextual terms and structurally complex linguistic constructions that do not contain contextual terms; and

structurally simple language constructs, which are structurally simple language constructs containing contextual terms, and structurally simple language constructs that do not contain contextual terms;

B) identify context terms in said source elements and form a database of context terms containing said identified context terms;

C) using the information contained in said generated database of contextual terms, structurally complex linguistic constructs containing contextual terms, structurally complex linguistic constructs not containing contextual terms, structurally simple linguistic constructs containing contextual terms are identified in said initial data structure , and structurally simple language constructs that do not contain contextual terms;

D) carry out the conversion of the first type on the above-mentioned structurally complex language constructs that do not contain contextual terms, to obtain grammatically and spelling-correct structurally simple language constructs that do not contain contextual terms;

E) carry out the conversion of the second type over the mentioned structurally simple language constructs containing contextual terms, to obtain grammatically and spelling correct structurally simple language constructs that do not contain contextual terms;

E) carry out the conversion of the third type over the above-mentioned structurally complex language constructs containing contextual terms, to obtain grammatically and spelling-correct structurally simple language constructs that do not contain contextual terms;

G) form the final data structure of the structured initial data array containing the elements of the final data structure, and the elements of the final data structure are structurally simple language constructs that do not contain contextual terms and obtained grammatically and spelling structurally correct due to transformations of the first, second and third types -simple language constructs that do not contain contextual terms.

In a second embodiment of the present invention, there is provided a method for converting a structured source data array containing at least natural language text, said method comprising at least the steps of:

A) form the initial data structure of a structured source data array containing the source elements of the original data structure, and the said source elements are:

structurally complex linguistic constructions, which are structurally complex linguistic constructions containing contextual terms and structurally complex linguistic constructions that do not contain contextual terms; and

structurally simple language constructs, which are structurally simple language constructs containing contextual terms, and structurally simple language constructs that do not contain contextual terms;

B) identify context terms in said source elements and form a database of context terms containing said identified context terms;

C) using the information contained in said generated database of contextual terms, structurally complex linguistic constructs containing contextual terms, structurally complex linguistic constructs not containing contextual terms, structurally simple linguistic constructs containing contextual terms are identified in said initial data structure , and structurally simple language constructs that do not contain contextual terms;

D) carry out the conversion of the first type on the above-mentioned structurally complex language constructs that do not contain contextual terms, to obtain grammatically and spelling-correct structurally simple language constructs that do not contain contextual terms;

E) carry out the conversion of the second type over the mentioned structurally simple language constructs containing contextual terms, to obtain grammatically and spelling correct structurally simple language constructs that do not contain contextual terms;

E) carry out the conversion of the third type over the above-mentioned structurally complex language constructs containing contextual terms, to obtain grammatically and spelling-correct structurally simple language constructs that do not contain contextual terms;

G) form the final data structure of the structured initial data array containing the elements of the final data structure, and the elements of the final data structure are structurally simple language constructs that do not contain contextual terms and obtained grammatically and spelling structurally correct due to transformations of the first, second and third types -simple language constructs that do not contain contextual terms; moreover

the first type conversion includes format conversion and linguistic conversion;

a second type conversion includes contextual conversion and linguistic conversion;

a third type conversion includes format conversion, contextual conversion, and linguistic conversion.

In a third embodiment of the present invention, there is provided a method according to any one of the first or second embodiments of the present invention, further comprising at least the steps of:

C) on the basis of the resulting final data structure form the first data structure of the structured source data array containing the elements of the said first data structure, said elements of the first data structure containing the first logical partitions and second logical partitions;

I) form a database of logical connections of logical sections of the mentioned elements of the first data structure;

K) form a second data structure of a structured source data array containing the elements of said second data structure, said elements of the second data structure containing logical structures of logical partitions of said elements of the first data structure formed using information from said logical linking database of logical partitions, said logical sections contain the first semantic parts and the second semantic parts;

K) form a database of semantic parts of logical sections from said second semantic parts, wherein said second semantic parts are excluded from the corresponding mentioned logical sections;

M) form grammatically and orthographically correct semantic parts of the mentioned logical sections by linguistic transformations over the mentioned semantic parts;

H) form the resulting data structure of the structured source data array containing the elements of the said resulting data structure, said elements of the resulting data structure containing logical constructs containing at least the grammatically and spelling-correct semantic parts of logical sections.

In a fourth embodiment of the present invention, there is provided a method according to a third embodiment of the present invention, characterized in that, in step 3), at least the steps are carried out in which:

identify the final data structure of the structured source data array;

identify elements of the resulting data structure;

identifying the first logical sections of said elements of the resulting data structure and the second logical sections of said elements of the resulting data structure; and

form the first data structure of the structured source data array containing the elements of said first data structure, said elements of the first data structure containing first logical partitions and second logical partitions.

In a fifth embodiment of the present invention, there is provided a method according to a third embodiment of the present invention, characterized in that in step I), at least the steps are carried out in which:

identifying elements of a first data structure containing one of said first logical partitions and elements of a first data structure containing one of said second logical partition;

identifying elements of a first data structure containing more than one of said first logical partitions, and elements of a first data structure containing more than one of said second logical partition;

among the elements of the first data structure containing more than one of said first logical partitions and in the elements of the first data structure containing more than one of said second logical partitions, logical relationships between said first logical partitions or between said second logical partitions are identified;

among the elements of the first data structure containing more than one of said first logical partitions and in the elements of the first data structure containing more than one of said second logical partitions, elements of the first data structure that do not have a logical connection between logical partitions are identified; and

form a database of logical relationships of logical sections of the elements of the first data structure.

In a sixth embodiment of the present invention, there is provided a method according to a third embodiment of the present invention, characterized in that, in step K), at least the steps are carried out in which:

form logical constructions of logical partitions of elements of the first data structure using information from a database of logical relationships of logical partitions of elements of the first data structure and logical partitions of said elements of the first data structure containing one of the first logical partitions and logical partitions of said elements of the first data structure containing one said second logical section; and

form a second data structure of a structured source data array containing elements of a second data structure, said elements of the second data structure being formed logical structures of logical partitions of the first data structure.

In a seventh embodiment of the present invention, there is provided a method according to a third embodiment of the present invention, characterized in that at step A), at least the steps are carried out in which:

identify the first logical partitions of the elements of the second data structure and the second logical partitions of the elements of the second data structure;

in said first logical sections and second logical sections of the elements of the second data structure, the first semantic parts and the second semantic parts are identified; and

in said first and second logical sections of the elements of the second data structure, at least the special semantic parts of the first logical sections of the elements of the second data structure and the special semantic parts of the second logical sections of the elements of the second data structure are identified and form a database of special semantic parts of the logical sections of the elements of the second structure data by moving said special semantic parts to said generated database of special semantic logical parts sections of the elements of the second data structure.

In an eighth embodiment of the present invention, there is provided a method according to a third embodiment of the present invention, characterized in that at step M), at least the steps are carried out in which:

in said second semantic parts, said second logical sections of the elements of the second data structure, at least clarifying structures of the second semantic parts of the second logical sections are identified; and

carry out linguistic transformations over all semantic parts, with the exception of the mentioned special semantic parts of the mentioned first and second logical sections, to form grammatically and spelling-correct semantic parts of logical sections of the elements of the second data structure.

In a ninth embodiment of the present invention, there is provided a method according to a third embodiment of the present invention, characterized in that, in step H), at least the steps are carried out in which:

form semantic combinations of grammatically and spelling-correct semantic parts of the second logical sections of the elements of the second data structure from the first grammatically and spelling-correct semantic parts of the second logical sections of the elements of the second data structure; and the grammatically and orthographically correct semantic parts of the second logical sections of the elements of the second data structure; and

form the resulting data structure of the structured source data array containing the elements of the resulting data structure, and said elements of the resulting data structure are logical constructs containing the grammatically and spelling-correct semantic parts of the logical sections of the elements of the second data structure.

In a tenth embodiment of the present invention, there is provided a method according to the ninth embodiment of the present invention, characterized in that said logical structures from said resultant data structure may further comprise said semantic combinations of grammatically and spelling-correct semantic parts of second logical sections of elements of the second data structure.

In an eleventh embodiment of the present invention, there is provided a method according to any one of the first or second embodiments of the present invention, characterized in that it further comprises at least the steps of:

H) identify the original data structure of the original structured data array; identify elements of the original data structure; identifying the first logical partitions of said elements of the original data structure and the second logical partitions of said elements of the original data structure; and form the first data structure of the structured source data array containing the elements of said first data structure, said elements of the first data structure containing first logical partitions and second logical partitions;

I) identify elements of the first data structure containing one mentioned first logical partition, and elements of the first data structure containing one mentioned second logical partition; identifying elements of a first data structure containing more than one of said first logical partitions, and elements of a first data structure containing more than one of said second logical partition; in the elements of the first data structure containing more than one of said first logical partitions and in the elements of the first data structure containing more than one of said second logical partitions, logical relationships between said first logical partitions or between said second logical partitions are identified; in the elements of the first data structure containing more than one of the first logical partitions and in the elements of the first data structure containing more than one of the second logical partitions, elements of the first data structure that do not have a logical connection between logical partitions are identified; and form a database of logical relationships of logical partitions of the elements of the first data structure;

K) form logical structures of logical partitions of elements of the first data structure using information from a database of logical connections of logical partitions of elements of the first data structure and logical partitions of said elements of the first data structure containing one of the first logical partitions and logical partitions of said elements of the first data structure, containing one said second logical partition; and form a second data structure of the structured source data array containing elements of the second data structure, said elements of the second data structure being formed logical structures of logical partitions of the first data structure;

K) identify the first logical partitions of the elements of the second data structure and the second logical partitions of the elements of the second data structure; in said first logical sections and second logical sections of the elements of the second data structure, the first semantic parts and the second semantic parts are identified; and in said first and second logical partitions of the elements of the second data structure, at least special semantic parts of the first logical partitions of the elements of the second data structure and the special semantic parts of the second logical partitions of the elements of the second data structure are identified and form a database of special semantic parts of the logical partitions of the elements of the second data structures by moving said special semantic parts to said logically generated database of special semantic parts x sections of the elements of the second data structure;

M) in said second semantic parts of said second logical sections of the elements of the second data structure, at least the refinement structures of the second semantic parts of the second logical sections are identified; and carry out linguistic transformations over all semantic parts, with the exception of the mentioned special semantic parts of the mentioned first and second logical sections, to form grammatically and spelling-correct semantic parts of logical sections of the elements of the second data structure;

H) form semantic combinations of grammatically and spelling-correct semantic parts of the second logical sections of the elements of the third structure from the first grammatically and spelling-correct semantic parts of the second logical sections of the elements of the second data structure and the grammatically and spelling-correcting refining structures of the second semantic parts of the second logical sections of the elements of the second data structure data; and form the resulting data structure of the structured source data array containing the elements of the resulting data structure, said elements of the resulting data structure being logical constructs containing the grammatically and spelling-correct semantic parts of the logical sections of the elements of the second data structure.

In a twelfth embodiment of the present invention, there is provided a method according to the eleventh embodiment of the present invention, characterized in that said logical constructs from said resultant data structure may further comprise said semantic combinations of grammatically and spelling-correct semantic parts of second logical sections of elements of the second data structure.

In this case, for a person skilled in the technical field to which the present invention relates, it should be obvious that the second to twelfth embodiments describe the specified steps of the method described by the first embodiment of the invention, and other embodiments of the invention can be implemented, and such other options embodiments of the invention will include various combinations of the specified process steps.

In a thirteenth embodiment of the present invention, there is provided a device for converting a structured source data array, comprising at least:

one or more processors;

one or more input / output (I / O) modules; and

a memory containing program code, which upon execution causes said one or more processors of said device and / or device associated with said device to perform the actions of the method according to any one of the first to twelfth embodiments of the present invention and containing one or more structured ones to be converted source data arrays containing at least natural language text.

In a fourteenth embodiment of the present invention, there is provided a device for converting an initial structured data array, comprising at least:

one or more processors;

one or more input / output (I / O) modules; and

a memory containing program code, which upon execution causes said one or more processors of said device and / or device associated with said device to perform the actions of the method according to any one of the first to twelfth embodiments of the present invention and containing one or more structured ones to be converted source data arrays containing at least natural language text, said one or more structured ones to be converted the source data arrays are downloadable, and said device is configured to connect to a database in which said downloadable one or more structured source data arrays to be converted are stored to load at least one loadable structured source transformable an array of data.

In a fifteenth embodiment of the present invention, there is provided a system for converting a structured source data array comprising at least:

one or more devices made in the form of devices according to any one of the thirteenth to fourteenth embodiments of the present invention;

one or more servers providing regulation of data exchange in said system;

one or more databases for storing data configured to interact with said one or more devices;

one or more data transmission networks through which said devices, servers and databases interact.

In a sixteenth embodiment of the present invention, there is provided a system for converting a structured source data array comprising at least:

one or more devices made in the form of devices according to any one of the thirteenth or fourteenth embodiments of the present invention;

one or more servers providing regulation of data exchange in the system;

one or more databases for storing data configured to interact with said one or more devices;

one or more data transmission networks through which said devices, servers and databases interact; moreover

the method according to any one of the first to twelfth embodiments of the present invention is carried out by one or more of said servers, and said devices are a thin client.

In a seventeenth embodiment of the present invention, there is provided a system for converting a structured source data array comprising at least:

one or more devices made in the form of devices according to any one of the thirteenth or fourteenth embodiments of the present invention;

one or more servers providing regulation of data exchange in the system;

one or more databases for storing data configured to interact with said one or more devices;

one or more data transmission networks through which said devices, servers and databases interact; moreover

the method according to any one of the first to twelfth embodiments of the present invention is carried out by one or more of said servers, and said devices are a thin client; moreover

said database is used to store data representing at least one of: a program code, which upon execution causes said one or more processors of said device and / or device associated with said device, to perform the actions of the method according to any one of the embodiments first to twelfth of the present invention, one or more structured source data arrays to be converted containing at least natural language text to be converted.

In an eighteenth embodiment of the present invention, there is provided a system according to any one of the fifteenth to seventeenth embodiments of the present invention, said data network being one of a local area network (LAN), wide area network (WAN), telecommunications network Internet, virtual private network (VPN).

In a nineteenth embodiment of the present invention, there is provided a computer-readable storage medium comprising program code that, upon execution, causes a processor or processors of a device with which the computer-readable storage medium interacts to perform the steps of a method as in any of the first to twelfth embodiments of the present invention.

In a twentieth embodiment of the present invention, there is provided a method of forming a map of links of components of grammatically and orthographically correct semantic parts of logical sections of logical structures of a converted structured source data array, said method comprising at least the steps of:

the components of the grammatically and spelling-correct semantic parts of the logical sections of the logical constructs from said transformed structured source data array are identified, each grammatically and spelling-correct semantic part containing said components containing at least one said component, said component containing no more than one component value, and form a component table containing at least the mentioned identifiers ized components and their meanings;

identify grammatically and orthographically correct semantic parts of logical sections of logical constructions containing said identified components, and grammatically and spelling correct semantic parts of logical sections of logical constructions that do not contain said identified components;

identify components contained in an amount of more than one in the aforementioned grammatically and spelling correct semantic parts, and identify combinations of components contained in an amount of more than one in each of the aforementioned grammatically and spelling correct semantic parts, and form a map of combinations of components containing at least said components contained in an amount of more than one in each separate grammatically and spelling-correct semantic part, and their etaniya;

identifying the values of the components contained in said combination map of the components;

in the grammatically and spelling-correct semantic parts of the logical sections of logical constructions that do not contain the identified components identified, the concepts are identified that semantically coincide with the mentioned component values contained in the component combination map, and a table of semantically matching concepts containing at least the component values is formed contained in the aforementioned map of combinations of components, and semantically coinciding concepts with them;

identify at least one grammatically and orthographically correct semantic part of the logical section of the logical structure that does not contain the identified components and contains more than one of the mentioned concepts;

identify the grammatically and orthographically correct semantic part of the logical section of the logical structure, containing more than one identified component mentioned, the values of which semantically coincide with the mentioned concepts, contained in one grammatically and spelling correct semantic part of the logical section of the logical structure, not containing the identified identified components and containing more than one the mentioned concept;

form a map of combinations of semantically matching concepts, containing at least the mentioned concepts, containing more than one in one grammatically and spelling correct semantic part of the logical section of the logical structure that does not contain the identified components, semantically matching the values of the identified components contained in one grammatically and spelling-correct semantic part of a logical section of a logical construction containing olee one of said identified component;

form a map of the relationships of the components of the grammatically and spelling-correct semantic parts of the logical sections of the logical constructions of the transformed structured source data array containing at least the said map of combinations of components and the mentioned map of combinations of semantically identical concepts.

In a twenty-first embodiment of the present invention, there is provided a method for generating a component link map according to the twentieth embodiment of the present invention, further comprising a step of generating a report showing at least the number of links and values of said components contained in the component combination map.

In a twenty-second embodiment of the present invention, there is provided a device for generating a map of links of components of grammatically and spelling-correct semantic parts of logical sections of logical structures of a transformed structured source data array, comprising at least:

one or more processors;

one or more input / output (I / O) modules; and

a memory containing program code, which upon execution causes said one or more processors of said device and / or device associated with said device to perform the actions of the method according to any of the embodiments of the present invention from twenty to twenty-first, and containing one or more transformed structured source data arrays.

In a twenty-third embodiment of the present invention, there is provided a device for generating a map of links of components of grammatically and spelling correct semantic parts of logical sections of logical structures of a transformed structured source data array, comprising at least:

one or more processors;

one or more input / output (I / O) modules; and

a memory containing program code, which upon execution causes said one or more processors of said device and / or device associated with said device to perform the actions of the method according to any of the embodiments of the present invention from twenty to twenty-first, and containing one or more transformed structured source data arrays, wherein said one or more transformed structured source data arrays are downloadable, and said device in is configured to connect to a database in which said downloadable one or more transformed structured source data arrays are stored to load at least one loadable converted structured source data array into said device memory.

In a twenty-fourth embodiment of the present invention, there is provided a system for generating a map of links of components of grammatically and spelling-correct semantic parts of logical sections of logical structures of a transformed structured source data array, comprising at least:

one or more devices made in the form of devices according to any one of the embodiments of the present invention from twenty-second to twenty-third;

one or more servers providing regulation of data exchange in said system;

one or more databases for storing data configured to interact with said one or more devices;

one or more data transmission networks through which said devices, servers and databases interact.

In a twenty-fifth embodiment of the present invention, there is provided a system for generating a map of relationships of components of grammatically and spelling-correct semantic parts of logical sections of logical structures of a transformed structured source data array, comprising at least:

one or more devices made in the form of devices according to any one of the embodiments of the present invention from twenty-second to twenty-third;

one or more servers providing regulation of data exchange in the system;

one or more databases for storing data configured to interact with said one or more devices;

one or more data transmission networks through which said devices, servers and databases interact; moreover

the method according to any one of the twenty to twenty first embodiments of the present invention is carried out by one or more of said servers, and said devices are a thin client.

In a twenty-sixth embodiment of the present invention, there is provided a system for generating a map of links of components of grammatically and spelling-correct semantic parts of logical sections of logical structures of a transformed structured source data array, comprising at least:

one or more devices made in the form of devices according to any one of the embodiments of the present invention from twenty-second to twenty-third;

one or more servers providing regulation of data exchange in the system;

one or more databases for storing data configured to interact with said one or more devices;

one or more data transmission networks through which said devices, servers and databases interact; moreover

the method according to any one of the twenty to twenty first embodiments of the present invention is carried out by one or more of said servers, and said devices are a thin client; moreover

said database is used to store data representing at least one of: a program code, which upon execution causes said one or more processors of said device and / or device associated with said device, to perform the actions of the method according to any one of the embodiments twenty to twenty first, one or more transformed structured source data arrays of the present invention.

In a twenty-seventh embodiment of the present invention, there is provided a system according to any one of the embodiments of the present invention from the twenty-fourth to the twenty-sixth, said data network being one of a local area network (LAN), wide area network (WAN), information telecommunication network, Internet virtual private network (VPN).

In a twenty-eighth embodiment of the present invention, there is provided a computer-readable storage medium comprising program code that, upon execution, causes a processor or processors of a device with which the computer-readable storage medium interacts to perform method steps according to any one of embodiments twenty to twenty-first.

In a twenty-ninth embodiment of the present invention, there is provided a method of searching for relevant information in a transformed structured source data array containing at least logical constructs containing grammatically and spelling-correct semantic parts of logical sections of logical constructs, said method comprising at least the steps , where:

form a search query containing at least one search term;

identifying the data array in said transformed structured source data array corresponding to said search term;

identifying at least one grammatically and spelling-correct semantic part of the logical sections of the logical constructs of said transformed structured source data array containing at least said search term;

at least one logical construction of said transformed structured source data array is identified, comprising at least said grammatically and spelling-correct semantic part;

demonstrate at least said identified logical construction of said transformed structured source data array containing at least said identified grammatically and spelling-correct semantic part.

In a thirtieth embodiment of the present invention, there is provided a search method according to a twenty-ninth embodiment of the present invention, further comprising the step of assigning at least one search criterion.

In the thirty-first embodiment of the present invention, there is provided a search method according to the thirtieth embodiment of the present invention, characterized in that the search criterion is at least one of: search in the first and second semantic parts of the first logical sections of logical structures and the second semantic parts of the second logical sections of logical constructions; search in the first semantic parts of the second logical sections of logical constructions and the second semantic parts of the second logical sections of logical constructions.

In the thirty-second embodiment of the present invention, there is provided a search method according to any one of the embodiments of the present invention from the twenty-ninth to the thirty-first, characterized in that the generated search query is at least one of: a terminological search query; component search query; component terminological search query.

In a thirty-third embodiment of the present invention, there is provided a search method according to any one of the embodiments of the present invention from the twenty-ninth to the thirty-first, further comprising at least the steps of:

selecting at least one of said identified logical constructs comprising at least said identified grammatically and spelling-correct semantic part;

at least grammatically and spelling-correct semantic parts associated with said grammatically and spelling-correct semantic parts are contained in other logical constructions of the transformed structured data array, said identification being carried out on the basis of information from the communications map of components of grammatically and spelling-correct semantic logical parts sections of logical constructions;

identify at least one or more logical constructs containing said identified grammatically and spelling-related semantic parts; or identify the absence of logical constructs containing said identified grammatically and spelling-related semantic parts;

demonstrate at least one or more logical constructs containing said identified grammatically and spelling-related correct semantic parts or demonstrate no logical constructs containing said identified grammatically and spelling-related semantic parts.

In a thirty-fourth embodiment of the present invention, there is provided a search method according to a thirty-third embodiment of the present invention, characterized in that the generated search query is at least one of: a terminological search query; component search query; component terminological search query.

In a thirty-fifth embodiment of the present invention, there is provided a device for searching for relevant information in a transformed structured source data array containing at least logical constructs containing grammatically and spelling-correct semantic parts of logical sections of logical constructs containing at least:

one or more processors;

one or more input / output (I / O) modules; and

a memory containing program code, which upon execution causes said one or more processors of said device and / or device associated with said device to perform the actions of the method according to any one of the embodiments of the present invention from the twenty-ninth to the thirty-fourth, and containing one or more converted structured source data arrays.

In a thirty-sixth embodiment of the present invention, there is provided a device for searching for relevant information in a transformed structured source data array containing at least logical constructs containing grammatically and spelling-correct semantic parts of logical sections of logical constructs containing at least:

one or more processors;

one or more input / output (I / O) modules; and

a memory containing program code, which upon execution causes said one or more processors of said device and / or device associated with said device to perform the actions of the method according to any one of the embodiments of the present invention from the twenty-ninth to the thirty-fourth, and containing one or more converted structured source data arrays, said one or more converted structured source data arrays being downloadable, and said The e device is configured to connect to a database in which said downloadable one or more transformed structured source data arrays are stored for loading at least one loadable transformed structured source data array into said device memory.

In a thirty-seventh embodiment of the present invention, there is provided a system for searching for relevant information in a transformed structured source data array containing at least logical constructs containing grammatically and spelling-correct semantic parts of logical sections of logical constructs containing at least:

one or more devices made in the form of devices according to any one of the embodiments of the present invention from thirty-fifth to thirty-sixth;

one or more servers providing regulation of data exchange in said system;

one or more databases for storing data configured to interact with said one or more devices;

one or more data transmission networks through which said devices, servers and databases interact.

In a thirty-eighth embodiment of the present invention, there is provided a system for searching for relevant information in a transformed structured source data array containing at least logical constructs containing grammatically and spelling-correct semantic parts of logical sections of logical constructs containing at least:

one or more devices made in the form of devices according to any one of the embodiments of the present invention from thirty-fifth to thirty-sixth;

one or more servers providing regulation of data exchange in the system;

one or more databases for storing data configured to interact with said one or more devices;

one or more data transmission networks through which said devices, servers and databases interact; moreover

the method according to any one of the embodiments of the present invention from the twenty-ninth to the thirty-fourth is carried out by one or more of said servers, and said devices are a thin client.

In a thirty-ninth embodiment of the present invention, there is provided a system for searching for relevant information in a transformed structured source data array containing at least logical constructs containing grammatically and spelling-correct semantic parts of logical sections of logical constructs containing at least:

one or more devices made in the form of devices according to any one of the embodiments of the present invention from thirty-fifth to thirty-sixth;

one or more servers providing regulation of data exchange in the system;

one or more databases for storing data configured to interact with said one or more devices;

one or more data transmission networks through which said devices, servers and databases interact; moreover

the method according to any one of the embodiments of the present invention from the twenty-ninth to the thirty-fourth is carried out by one or more of said servers, and said devices are a thin client; moreover

said database is used to store data representing at least one of: a program code, which upon execution causes said one or more processors of said device and / or device associated with said device, to perform the actions of the method according to any one of the embodiments twenty-ninth to thirty-fourth of the present invention, one or more transformed structured source data arrays.

In the fortieth embodiment of the present invention, there is provided a system according to any one of the embodiments of the present invention from thirty-seventh to thirty-ninth, said data network being one of a local area network (LAN), wide area network (WAN), information and telecommunication network Internet, virtual private network (VPN).

In a forty-first embodiment of the present invention, there is provided a computer-readable storage medium comprising program code that, upon execution, causes a processor or processors of a device with which the computer-readable storage medium is interacting to perform method steps according to any one of embodiments of the present invention from twenty-ninth to thirty-fourth.

DETAILED DESCRIPTION OF THE DRAWINGS

The possible implementations of the embodiments of the present invention described in this section are presented in non-limiting examples, with reference to specific embodiments of the present invention, which in all their aspects are assumed to be illustrative and not limiting. Alternative embodiments of the present invention, not beyond the scope of its legal protection, are obvious to experts in this field with the usual qualifications on which this invention is intended.

In FIG. 1, by way of example, but not limitation, a general flowchart of the steps of the claimed method 100 for transforming a structured source data array containing at least natural language text is depicted. The claimed method 100 for converting a structured source data array containing at least natural language text is characterized by performing step 101 of generating an initial data structure of a structured source data array, on which an initial data structure of a structured source data array containing the original elements of the original data structure is formed, moreover, the said initial elements are structurally complex linguistic constructions consisting of structurally complex linguistic constructs containing contextual terms, and structurally complex linguistic constructs not containing contextual terms, and structurally simple linguistic constructs containing structurally simple linguistic constructs containing contextual terms, and structurally simple linguistic constructs that do not contain contextual terms. In step 101 of generating the initial data structure of the structured source data array, an analysis of the elements of the structured source data array is performed to determine if it contains the source elements of the original data structure of the structured source data array. As a result of the analysis, the initial data structure of the structured initial data array is formed, which consists of structurally complex and structurally simple language structures - sentences and enumeration headings. The identification of structurally complex language structures is carried out in accordance with the first identification criterion, which is an identification criterion for the first type of combination of punctuation marks. The first type of combination of punctuation marks is characterized by the presence of the following features of a structurally complex language structure:

a) structurally complex linguistic construction consists of several text fragments;

b) in the first text fragment there is the last character of the first text fragment - the punctuation mark “:” (“colon”);

c) after the punctuation mark “:” there is a formatting character (typographic character) - “¶” (“paragraph”);

d) after the punctuation mark “:” of the first text fragment, the second text fragments are located, each of which is located on a new line - the red line of the paragraph;

e) each of the second text fragments begins with a capital letter;

g) each of the second text fragments can begin with a list marker - a formatting character (typographic sign) used to highlight list items (listing headings);

h) each of the second text fragments may begin with a numbering marker - numbers or letters, with a parenthesis or period;

i) the number of second text fragments located on the red lines of paragraphs must be at least two.

At the same time, structurally simple language constructs are those elements of the initial data structure in which the combination of punctuation marks does not meet the first criterion, i.e. such elements of the original data structure satisfy the identification criterion for the second type of punctuation mark combination, the second type of punctuation mark combination being all punctuation mark combinations, except for punctuation mark combinations of the first type. The identification of structurally simple linguistic constructions is carried out by identifying in the natural language text the signs of the end of the sentence - punctuation marks “.” (“Dot”), “...” (“ellipsis”), etc., in conjunction with the signs of the beginning of the next sentences - capital letters, numbers, etc., taking into account the presence and certain combinations of punctuation marks, namely punctuation marks (“.” (“dot”), “;” (“semicolon”), “...” (“Ellipsis”), “,” (“comma”), “(” (“opening bracket”), “)” (“closing bracket”), “:” (“colon”), etc.), from water separators (“” (“space”), “-” (“dash”), etc.) and typography (“¶” (“paragraph”), number, “°” (“degree”), etc. P.).

Also, the method 100 for converting a structured source data array is characterized by performing a step 102 of identifying context terms and forming a database of context terms, where context terms are identified in said source elements and forming a context terms database containing said identified context terms. At this stage, combinations of contextual terms of the first type, contextual terms of the second type, contextual terms of the third type, which as a result are identified as contextual concepts, contextually generalized concepts and contextual links, are identified. Identification by the criterion of a combination of contextual terms of the first type allows the identification of contextual concepts. Contextual concepts are a one-word or verbose term that does not have an independent semantic meaning, the true meaning of which is clarified with the help of another, synonymous or associative term (group of terms), located in the same sentence as the contextual concept, or in previous sentences. Identification by the criterion of a combination of contextual terms of the second type allows the identification of contextually generalized concepts. Contextually generalized concepts are single-word or verbose terms, one of which is a generalizing word, and the other is generalized words, while the term being a generalizing word includes the lexical meaning of a number of homogeneous members (generalized words) and responding to the same the question is that of homogeneous members. Identification by the criterion of a combination of contextual terms of the third type allows the identification of contextual links. Contextual links are a verbose term that does not have an independent semantic meaning, which explicitly or implicitly refers to other sentences that contain the semantic meaning of this term. In this case, the link in which the exact location of the semantic meaning is indicated (for example, the requirements specified in Article 12, paragraph 2 of the Law of the Russian Federation of 07.02.1992 N 2300-1 “On Protection of Consumer Rights” - “Seller (executor ), which did not provide the buyer with complete and reliable information about the product (work, service), is liable under paragraphs 1-4 of article 18 or paragraph 1 of article 29 of this Law for defects in the goods (work, service) that arose after its transfer to the consumer due to his lack of it oh information. ”). If the contextual link does not indicate the exact location of the meaning, then such a link is an implicit contextual link, and the “degree of implicitness” of the implicit contextual link is inversely proportional to the value of the “degree of accuracy” of the location of the meaning. An example of an implicit contextual link with a “maximum degree of implicitity” is a contextual link of the form “specified requirements”. The identification of contextual terms reveals, in fact, contextual terms and terms that clarify their semantic meaning. Between contextual terms and terms that clarify their semantic meaning, a contextual relationship is recorded, which is the reason for the formation of the corresponding entries in the database of contextual terms. Based on the information contained in the database of contextual terms, it becomes possible to carry out special transformations (linguistic and other transformations) over sentences containing contextual terms in order to ensure clarity and unambiguity of understanding of contextual terms. Contextual terms identified in sentences are not necessarily removed from sentences and may remain in them, but their meaning will be clear and unambiguous, as this meaning will be clarified in the relevant contextual reference books (reference book of contextual concepts, reference book of contextually generalized concepts, reference book of contextual links).

Also, the method 100 for converting a structured source data array is characterized by performing a step 103 of identifying the elements of the original data structure, in which, using the information contained in said generated database of context terms, structurally complex language constructs containing context terms are identified in said initial data structure, structurally complex linguistic constructions that do not contain contextual terms, structurally simple linguistic constructions containing conte stnye terms and structurally simple language constructs that do not contain contextual terms. Based on the information contained in the database of contextual terms, the initial elements of the initial data structure of the structured initial data array are identified. At this stage, an unambiguous identification of the source elements and their grouping is carried out according to the signs of the presence or absence of contextual terms in them and the type of the source element. Thus, structurally complex linguistic constructs containing contextual terms, structurally complex linguistic constructs not containing contextual terms, structurally simple linguistic constructs containing contextual terms, and structurally simple linguistic constructs not containing contextual terms are uniquely identified.

Also, the method 100 for converting a structured source data array is characterized by performing a first type conversion step 104, where the first type is converted over said structurally complex language constructs that do not contain contextual terms to obtain grammatically and spelling-correct structurally simple language constructs that do not contain contextual terms. Structurally complex linguistic constructions that do not contain contextual terms are subjected to transformations with the aim of transforming enumeration headings into sentences. In the process of such transformations, structurally complex linguistic constructions that do not contain contextual terms are converted into structurally simple linguistic constructions that are sentences in the usual sense of the term. This conversion is initially formatted in nature, i.e. non-formatted text of a structurally complex linguistic structure is converted into formatted, i.e. corresponding to the grammatical concept of the sentence. To eliminate grammatical and spelling errors associated with the conversion of structurally complex language structures that do not contain contextual terms into structurally simple language structures, linguistic transformations are carried out. Linguistic transformations over created structurally simple linguistic constructions are associated with the restoration of correct grammar and spelling. The indicated linguistic transformations are understood as: a) the coordination of sorts, numbers, cases; b) editing (replacement and removal) of inappropriate punctuation marks. All the above transformations performed on structurally complex language constructs that do not contain contextual terms are transformations of the first type.

Also, the method 100 for converting a structured source data array is characterized by performing a second type conversion step 105, in which the second type is converted over said structurally simple language constructs containing contextual terms to obtain grammatically and spelling-correct structurally simple language constructs that do not contain contextual terms. Structurally simple language constructs containing contextual terms are transformed in order to structure and clarify the semantic content of contextual terms. In the process of such transformations, structurally simple linguistic constructions containing contextual terms are transformed into grammatically and spelling-correct structurally simple linguistic constructions that do not contain contextual terms. This transformation is structurally linguistic in nature, i.e. it is associated with the actual replacement or removal from the text of a structurally simple language construct containing contextual terms, all or part of contextual terms and the actual replacement of deleted terms with other terms that are the semantic content of contextual terms. In addition, the mentioned transformation may be associated with the addition of the text of a structurally simple language construct containing contextual terms, the formation of a new entry in the corresponding contextual reference. Each type of contextual term applies its own type of transformation. So for contextual concepts, the first transformation of the second type is used, for contextually generalized concepts - the second transformation of the second type, for contextual references - the third transformation of the second type. According to the results of transformations of contextual terms, a linguistic conversion is carried out to eliminate grammatical and spelling errors associated with the conversion of structurally simple language constructs containing contextual terms into structurally simple language constructs that do not contain contextual terms. Linguistic transformations over created structurally simple language constructs that do not contain contextual terms are associated with the restoration of correct grammar and spelling. The indicated linguistic transformations are understood as: a) the coordination of sorts, numbers, cases; b) editing (replacement and removal) of inappropriate punctuation marks. All of the above transformations are transformations of the second type. The first transformations of the second type are carried out over contextual concepts and revealed terms that clarify the semantic meaning of contextual concepts. These identified terms can be either contextual synonyms or contextual associations. A context synonym is a synonym that can be used as a synonym only in this context. For example, in the sentence: “A manufacturer is an organization regardless of its legal form,” the contextual term “her” is, in fact, a contextual concept, and the identified term “organization” is its contextual synonym. The first transformation of the second type provides the formation of a directory of contextual concepts, which, in turn, provides clarity of the semantic meaning of contextual concepts. Also, due to the first transformation of the second type, linguistic text correction is provided by replacing contextual concepts with a contextual synonym or contextual association. For example, the sentence: “A manufacturer is an organization regardless of its legal form”, through the implementation of the first transformation of the second type on it will be transformed into a sentence: “A manufacturer is an organization regardless of the legal form of organization”. Contextual associations differ from contextual synonyms in that they, as a rule, represent a more complex text structure, consist of several terms and are rarely found in one sentence with a contextual concept, but more often in other sentences. The second transformations of the second type are carried out over contextually generalized concepts. Moreover, context-generalized concepts mean a generalizing word (generalizing term) and homogeneous words (homogeneous terms), as well as a group of homogeneous words (a group of words without a generalizing word). A generalizing word is a word or phrase that is common to homogeneous words or phrases located near the generalizing word. In this case, homogeneous words (phrases) in the context of the present invention are those members of the sentence that depend on the same word and answer the same question (i.e. they are homogeneous members of the sentence). Moreover, a generalizing word is the same type of sentence members as words or phrases homogeneous with it. A significant part of the proposals complicated by homogeneous members can be presented as a result of the “compositional reduction” of a number of independent proposals. The second transformation of the second type removes all terms that are homogeneous with the generalizing word from the analyzed sentence, due to which only the term that is the generalizing term remains in the sentence. In this case, a directory of contextually generalized concepts is formed, which contains the identified generalizing term and all the homogeneous words corresponding to it - generalized words (generalized concepts). In addition, the second transformation of the second type provides a linguistic adjustment of the sentence by creating new sentences that use generalized concepts instead of a generalizing term. For example, in the sentence: “The Contractor is an organization that performs work or provides services to consumers under a reimbursable contract”, there are homogeneous words (homogeneous terms) - the term “performing work” and the term “providing services”. In this example, due to the second transformation of the second type, an entry will be generated in the directory of contextually generalized concepts containing an indication that the term “performing work” is a generalizing term, and the term “providing services” is a “generalized concept”. In this case, the generalized concept, i.e. the term "providing services". Thus, the converted proposal will be simplified and converted into a proposal: “The Contractor is an organization that performs work for consumers under a reimbursable contract.” By means of the second conversion of the second type, an additional sentence is formed in which, instead of the term “performing work”, the term “providing services” is present. Thus, instead of a single sentence with contextually generalized terms, two sentences are formed without contextual terms, namely, the sentence “Contractor is an organization that performs work for consumers under a reimbursable contract” and the sentence “Contractor is an organization that provides services to consumers under a reimbursable contract” . The third conversion of the second type is performed on contextual links. Methodologically, the third transformation of the second type is similar to the first transformation of the second type carried out on contextual concepts. However, in this case, the semantic meaning of the contextual link is determined differently. The fundamental difference between the first conversion of the second type and the third conversion of the second type is that in a sentence that contains a contextual link, as a rule, it is obvious what exactly is the subject of the contextual link. For example, for a proposal with an implicit contextual link - “specified requirements”, it is obvious that the subject of the contextual link is some “requirements”. Moreover, often offers contain more detailed information about the subject of a contextual link (meaning). By means of the third conversion of the second type, as in the case of the first conversion of the second type, the formation of a contextual reference guide is provided, which allows one to understand the semantic meaning of the contextual link. However, unlike the first conversion of the second type, by means of the third conversion of the second type, a linguistic correction of the sentence by replacing the contextual link with its semantic value cannot be carried out mainly because the semantic value significantly exceeds the contextual link in terms of character size.

Also, the method 100 for converting a structured source data array is characterized by performing a third type conversion step 106, in which the third type is converted over said structurally complex language constructs containing contextual terms to obtain grammatically and spelling-correct structurally simple language constructs that do not contain contextual terms. A transformation of the third type is a combination of transformations of the first type and transformations of the second type, applied to structurally complex language constructs containing contextual terms. The conversion of the third type is a two-stage conversion. At the first stage, structurally complex linguistic constructions containing contextual terms are converted to the first type, due to which the structurally complex linguistic construct containing contextual terms is converted to a structurally simple linguistic construct containing contextual terms. At the second stage, structurally simple linguistic constructions containing contextual terms are subjected to the corresponding transformation of the second type. As a result of a two-stage transformation, structurally complex linguistic constructions containing contextual terms are converted into grammatically and spelling-correct structurally simple linguistic constructions that do not contain contextual terms.

Also, the method 100 for converting a structured source data array is characterized by performing step 107 of generating the final data structure of the structured source data array, which forms the final data structure of the structured source data array containing elements of the final data structure, the elements of the final data structure being structurally simple language constructions that do not contain contextual terms and obtained through transformations of the first, second, and third of the types of grammatically and spelling correct structurally simple language constructs that do not contain contextual terms. The final data structure is formed from: structurally simple language constructs that do not contain contextual terms; structurally simple language constructs that do not contain contextual terms obtained by converting structurally complex language constructs that do not contain contextual terms; structurally simple language constructs that do not contain contextual terms obtained by converting structurally simple language constructs containing contextual terms; structurally simple language constructs that do not contain contextual terms, obtained by converting structurally complex language constructs containing contextual terms. In fact, the final data structure consists of elements — grammatically and spelling-correct structurally simple language constructs that do not contain contextual terms, which from a linguistic point of view are sentences that do not contain contextual terms.

In FIG. 2 as an example, but not limitation, depicts a General diagram of the steps of the claimed method 200 for converting a structured source data array containing at least natural language text. The claimed method 200 for converting a structured source data array containing at least natural language text, in contrast to the method 100 for converting a structured source data array, is further characterized by the execution of step 201 of generating the first data structure, based on which obtained on the basis of formation 107 the final data structure of the structured source data array form the first data structure of the structured data array containing the elements yanutoy first data structure, said elements of the first data structures representing the final elements of the data structure of a structured data source array comprise first and second logical partitions are logical partitions; performing step 202 of forming a database of logical connections of logical partitions, on which a database of logical connections of logical partitions of said elements of the first data structure is formed; performing step 203 of generating a second data structure, which forms a second data structure of a structured data array containing elements of said second data structure, said elements of the second data structure containing logical structures of logical partitions of said elements of the first data structure generated using information from said database logical connections of logical sections, moreover, the mentioned logical sections contain the first semantic parts and second semantic part; performing step 204 of forming a database of semantic parts of logical partitions, on which a database of semantic parts of logical partitions from said second semantic parts is formed, said second semantic parts being excluded from corresponding logical partitions; performing step 205 of forming grammatically and spelling correct semantic parts, in which grammatically and spelling correct semantic parts of said logical sections are formed by linguistic transformations over said semantic parts; and performing step 206 of generating the resulting data structure, which forms the resulting data structure of the structured data array containing the elements of said resultant data structure, said elements of the resulting data structure containing logical constructs containing at least the grammatically and spelling-correct semantic parts of the logical sections.

In FIG. 3, by way of example, but not limitation, a general flowchart of the steps of step 201 of generating the first data structure is shown. Step 201 is characterized by performing step 2011 of identifying the source structure, in which the resulting data structure 1 of the structured data array is identified; performing element identifying step 2012, wherein elements 11 of the final data structure 1 are identified; the implementation of the stage 2013 identification of logical partitions, which identify the first logical partitions 111 of the elements 11 of the final data structure 1 and the second logical sections 112 of the elements 11 of the final data structure 1; and the implementation of the stage 2013 of the formation of the first data structure, which forms the first data structure 2 of the structured source data array containing the elements 21 of the first data structure, which are elements 11 of the final data structure 1, the elements 21 of the first data structure 2 containing the first logical sections 111 elements 11 of the final data structure 1, and containing elements 22 of the first data structure, and elements 22 of the first data structure 2 contain second logical sections 112, elements 11 of the final structure 1 data.

In FIG. 4, by way of example, but not limitation, the general structure of the final data structure 1 is shown, from which the first data structure 2 is formed. The final data structure 1 is the final data structure obtained at step 107, which, in turn, is a structured data structure An initial data array containing at least natural language text. As mentioned above, such an array of data may constitute, in particular, a regulatory legal act (NLA). The resulting data structure 1 contains elements 11, which are positions that are sentences - grammatically organized word combinations. Moreover, each sentence is characterized by semantic completeness, because is an element of the final data structure obtained at step 107. The identification of proposals at the stage of 2012 identification of elements is carried out by identifying in the natural language text signs of the end of the sentence. Signs of the end of the sentence are: period, semicolon, ellipsis, etc. Identification of proposals is carried out in conjunction with the identification of signs of the beginning of the proposal. Signs of the beginning of a sentence are: a capital letter, a number, a number with a closing bracket, a number with a period, etc. Moreover, the identification also takes into account the presence of certain combinations of punctuation marks, namely punctuation marks - periods, semicolons, brackets, colons, etc., - word separators, namely space, etc., and typography - paragraph, number , degrees, etc. At the stage 2012 of the identification of elements, the identification of elementary semantic units of sentences, which are judgments, is carried out. A simple judgment is a judgment, no part of which is a judgment. The linguistic form of expression of judgment is narrative sentences. The identification of elements 11 of the final data structure 1 is carried out by identifying and defragmenting the sentence into the primary components of the sentence, namely into words, particles, conjunctions, prepositions, etc., and punctuation marks. After that, concepts expressed in separate words and / or phrases based on various directories and dictionaries are formed from the primary elements. Then, simple judgments are formed from the formed concepts, which are groups of interconnected concepts, while the interconnectedness of concepts is determined on the basis of syntactic or other relationships between the concepts. To form simple judgments, a linguistic-semantic analysis of elements 11 of the final data structure 1 is carried out, whereby elements 11 of simple judgments are identified in elements 11. The structural elements of simple judgments are understood as the subject of judgment, the predicate of judgment, the connective and the quantifier word. The subject of judgment (S) is the concept expressing the subject of judgment, i.e. what is said in this statement. The predicate of judgment (P) is a concept that expresses this or that information about the subject of judgment. The subject of judgment and the predicate of judgment are the basic structural elements of judgment, which are terms of judgment. The connection between the subject of judgment and the predicate of judgment, which reflects the real relationship between objects conceivable in concepts, is revealed through a logical connective. In Russian, the connective is expressed by the words: “is” (“is not”), “is” (“is not”), “is” (“is not available”), etc., is indicated by a dash, a colon, and can also be implied by the agreement of words (“It is raining”, “The dog barks”). A connective is a logical constant, because it contains unchanged content - it always serves as an indicator of the presence or absence of something in the subject of thought. A quantifier word (for example, “everyone”, “all”, “none”, “some”, etc.) indicates whether the information on the predicate of a judgment applies to the entire volume of a concept expressing a subject of a judgment, or to its part. For example, in the proposition “Every crime is an unlawful act”, the subject of the judgment is the concept of “crime”, the predicate of the judgment is “unlawful act”, the connective is expressed by a dash, and the quantifier word “any” indicates that the characteristic “unlawful act” refers to the entire volume (to each element) of the concept of “crime”. In its most general form, a simple proposition can be expressed by the formula: “S is (not is) P”. Thus, as a result of the identification of the final data structure 1, the elements 11 are divided into the number of judgments that are embedded in them, i.e. the number of judgments is equal to the number of logical sections in the sentence. Next, the identification of the first logical sections 111 and the second logical sections 112 of the elements 11 of the final data structure 1. The identification is based on the results of the identification of the identified judgments, defined as complex judgments. Complex judgments are a group of simple judgments in which there is a connection between separate judgments, established using logical conjunctions "and", "or", "if ... then ...", "if and only if ... when", "it’s wrong, what…". Types of relationships between individual judgments are expressed by the corresponding logical connectives and are shown in Table 1.

The nature of the connection is determined by the meaning of logical alliances, which consists in answering the question: “Under what conditions will a complex judgment be true, and under which it will be false?” In other words, under what combinations of truth and falsity of simple judgments that make up a complex proposition, a logical union determines the true connection, and in which - the false one. A judgment is considered true if the description it gives is true (a real situation), and false if it does not. “Truth” and “false” are called truth values of judgment and are the main logical characteristic of judgments. The meaning of logical alliances can be determined using the truth table (Table 2), in which columns 1 and 2 contain all possible combinations of truth values of simple judgments, and columns 3-9 contain the values of a complex proposition formed from simple judgments using the corresponding logical union . In this case, the initial simple judgments are denoted by the letters "A", "B", and the truth values by the symbols: "and" - true, "l" - false.

In order to identify the first logical sections 111 and the second logical sections 112 of the elements 11 of the mentioned final data structure 1, it is necessary to identify simple propositions in the text sentences that have an implicative (conditional) logical connection and mutual (double) implicative (conditional) connection. A conditional proposition (implicative proposition) is a complex proposition in which simple propositions are united by the logical union “if ... then ...”. For example:

“If a citizen violates the law, this creates liability for the violation” or “If the number is divisible by 2 without a remainder, then it is even.” A conditional proposition consists of two types of judgments that comprise it. The judgment written after the word “if” is called the basis (previous). The judgment written after the word "that" is called the consequence (subsequent). The conditional propositional formula can be represented as “A → B”, where A is the basis, B is the consequence. At the same time, the foundations and consequences, in themselves, can be both simple judgments and complex judgments. The conditional proposition formed from the previous and subsequent judgments, first of all, implies that it cannot be that what is said in the basis takes place, but what is said in the investigation is absent. In other words, if the ground is true and the effect is false, then such a conditional proposition will be false. This condition determines that a conditional proposition is true in all cases except one: when the previous one is, and the next is not, i.e. - a judgment by the formula “A → B” is false only in one case, when A is true and B is false (see table 2, column 6). In the form of conditional judgments can be expressed as the objective dependence of some objects on others, as well as the rights and obligations of subjects of legal relations related to certain conditions. Equivalent judgment (double implication) is a complex proposition in which propositions with mutual conditional dependence are combined. Equivalent judgments are formed using the logical union “if and only if ... then ...”, which is indicated by the symbol “↔”. Equivalence formula: “A↔B”, where A, B are the judgments from which the equivalent judgment is formed, for example: “A person has the right to an old-age pension if and only if he has reached retirement age”. In the natural language, including economic and legal texts, grammatical unions are used to express equivalent judgments: “only if ..., then ...”, “only when ..., then ...”, “and only in the case when ... then ... ". The truth conditions for equivalent judgments are presented in column 7 of table 2. An equivalent proposition is true in two cases - when both components of its judgments are true or when both are false. In other words, the connection between the elements of an equivalent judgment can be described as necessary: "the truth of A is sufficient to recognize the truth of B and vice versa" and "the falsity of A serves as an indicator of falsity of B and vice versa." Due to the fact that double implicative judgments do not have clear grounds and consequences, the main factor in identifying the first and second logical sections 111, 112 is the presence in judgments that are in the mutual implicative dependence of the signs of a legal fact. On the example of the mentioned implicative judgment “A person has the right to an old-age pension if and only if he has reached retirement age”, it can be established that the judgment “A person has a right to an old-age pension” does not contain such signs, and a judgment “if and only if he has reached retirement age ”has a sign of legal fact, which is the event -“ reaching retirement age ”. Thus, it is a judgment containing signs of a legal fact that is recognized for identification purposes by reason (A). A different judgment is recognized by consequence (B). The term "legal fact" means a specific circumstance of life with which a rule of law relates the occurrence, change or termination of a legal relationship or legal relationship. If a sentence contains one simple proposition (or several simple propositions) or one complex proposition (or several complex propositions) that is not identified as a conditional proposition, then a linguistic-semantic analysis of the text “surrounding” such an proposition may reveal the actual contextual the form of this simple proposition is the first logical section 111 or the second logical section 112. The first data structure 2 formed in step 201 contains elements such as sentences (element 11 of the original structure data 1) and the judgment (logical partitions 111 or 112 elements of source data structures 11 1). Moreover, judgments are additionally identified by the mentioned logical connection as grounds, i.e. as the first logical section 111 of the element of the initial data structure 11, which has a logical connection of the 1st type and is the judgment “A”, and as a consequence, i.e. as the second logical section 112 of element 11 of the original data structure 1, having a logical connection of the 1st type and which is the judgment “B”. In the first data structure 2, all elements of the original data structure 1 are separated by the presence of the aforementioned first logical partitions 111 or second logical partitions 112 of the original data structure 1, whereby the elements 21 of the first data structure 2 are formed having the first logical partitions 111 and elements 22 of the first data structure 2 having second logical partitions 112.

In FIG. 5, by way of example, but not limitation, a general flowchart of the steps of step 202 of forming a database 3 of logical connections of logical partitions is shown. The step 202 of creating a database 3 of logical connections of logical partitions is characterized by the execution of step 2021 of identifying the elements of the first data structure 2, which identifies the elements 21 of the first data structure 2 containing one of the first logical partitions 111, which are the elements 31 of the first data structure 2, and the elements 22 of a first data structure 2, comprising one of said second logical partitions 112, which are elements 32 of a first data structure 2; performing step 2022 of identifying the elements of the first data structure 2, which identifies the elements 21 of the first data structure containing more than one of the first logical partitions 111, which are the elements 33 of the first data structure 2 and the elements 22 of the first data structure containing more than one of the second logical partitions representing the elements 34 of the first data structure 2; by performing step 2023 of identifying logical connections, where among the elements 33 of the first data structure 2 containing more than one of the first logical partitions 111 and the elements 34 of the first data structure 2 containing more than one of the second logical part 112, the logical relationships between the first logical partitions are identified 111 or logical connections between said second logical partitions 112; by performing step 2024 of identifying the absence of logical connections, where among the elements 33 of the first data structure 2 containing more than one of the first logical partitions 111 and among the elements 34 of the first data structure 2 containing more than one of the second logical partitions 112, the elements 35 of the first data structure are identified * having no logical connections between their logical partitions; and the implementation of step 2025 of the formation of the database, which form the database 3 logical relationships of logical sections of the elements of the first data structure. To clarify all additional (not only implicative) logical connections between logical partitions (judgments), all elements of the first data structure 2, namely arrays of elements of 21 sentences containing the first logical sections 111 and elements of 22 sentences containing the second logical sections 112, must be separated into groups of elements 31, 33 and 32, 34, containing either only the first logical partitions 111, or only the second logical partitions 112, respectively. In this case, each element included in the arrays of elements 31, 33 containing the first logical partition 111 is identified as element 31. having only one logical partition 111, or as element 33 having more than one logical partition 111. In this case, if the identified elements are present 31, 33 of the second logical partitions 112, the second logical partitions 112 are deleted from the identified elements 31, 33. The resulting arrays of elements 31, 33 are still associated with the element from which they are selected and identified on this basis ak individual elements of the array elements. In turn, each element in the arrays of elements 32. 34 containing the second logical partition 112 is identified as element 32, which has only one logical partition 112, or as element 34, having more than one logical partition 112. Moreover, if identified elements 32, 34 of the first logical partitions 111, the first logical partitions 111 are deleted from the identified elements 32, 34. The resulting arrays of elements 32, 34 are also still associated with the element from which they are selected and based on this are identified as separate elements of a given array of elements. Next, the nature of the logical relationships between the same judgments in the elements of two created arrays of elements 31, 33 and the elements 32, 34 is established. In the same elements of the arrays of elements 33, 34, logical relationships between the judgments shown in tables 1 and 2 are revealed, namely, connecting connections ( conjunction), separation bonds (disjunction, strict disjunction), equivalent bonds (equivalent). A conjunctive (connecting) proposition is a complex proposition formed from the initial propositions by means of a logical union “and”, denoted by the symbol “^”. For example, the proposition: “Today I will go to a lecture on logic and in cinema,” is a “conjunctive proposition” consisting of two simple propositions (we denote them by A and B, respectively) - “Today I will go to a lecture on logic” (A ), “Today I will go to the cinema” (B). This complex proposition can be represented by the formula: “A ^ B”, where A, B are the elements of the conjunction; "^" - a symbol of logical union - conjunction. In Russian, the conjunctive logical union is expressed by many grammatical unions: “and”, “a”, “but”, “yes”, “although”, “however”, “as well as ...”. Often such grammatical unions are replaced by punctuation marks - a comma, a colon, a semicolon. A disjunctive (dividing) proposition is a complex proposition formed from the “source” ones. judgments through the logical union “or”, denoted by the symbol “V”. For example, the proposition: “The law can contribute to economic development or hinder it,” is a disjunctive proposition consisting of two simple judgments: “The law can contribute to economic development” and “The law can impede economic development”. Accordingly, having designated them through the letters A, B, such a judgment can be represented through the formula: “A V B”. Since the connective "or" is used in two different meanings - non-exclusive and exclusive, they distinguish between weak and strong (strict) disjunctions. A weak disjunction is true when at least one of its component judgments (or both together) is true and false when both its judgments are false (see table 2, column 4). A strong disjunction (the symbol “VV”) differs from a weak disjunction in that its components are mutually exclusive. For example, “Crime may be intentional or negligent.” In order to emphasize the strictly dividing, excluding the nature of the connection, a reinforced double form of separation is used in the natural language: "... either ..., or ...", "... or ..., or ...", for example: "Either I will find the way, or I will pave him". A strict disjunction is true only when one of its component judgments is true and the other is false (see table 2, column 5). As a result, all logical connections between the elements (judgments) of the arrays of elements 33, 34 are revealed. It is assumed that some of the elements of these arrays may not have logical connections with each other. Further, to clarify the type of judgments that have not yet been identified as complex judgments, the elements of the arrays of elements must be identified for their conformity to the denied judgment. Arrays of elements 31, 32 and, in part, arrays of elements 33, 34, in which there are judgments that have no logical connections with other judgments, should be subjected to this analysis. A denied proposition is a complex proposition formed by the logical union “it is not true that ...” (or simply “not”), which is usually represented by a negative sign (“~” symbol). Unlike the binary unions mentioned above, such an alliance refers to one proposition. Adding this union to a proposition means the formation of a new proposition, which depends on the original proposition - the denied proposition is true if the original proposition is false, and vice versa (see table 2, columns 8, 9). For example, if the initial judgment is: “All witnesses are truthful,” then the denied: “It is not true that all witnesses are truthful.” If a separate logical section (simple proposition) remains unidentified from the point of view of the logical nature of the proposition, then as a result of linguistic-semantic analysis of the text surrounding the sentence containing such a section, the actual contextual form of this simple proposition can be revealed. Thus, the first data structure contains logical sections of sentences that form the original data structure. According to the results of identification of all elements of the first data structure, all logical sections of sentences (judgments) are identified, in terms of their presence and the nature of the relationships of judgments forming complex judgments. Based on the identified nature of the relationships of judgments (including their absence), a database of 3 logical relationships of logical sections is formed (Fig. 6).

In FIG. 7, by way of example, but not limitation, a general flowchart is shown of the steps of step 203 of generating the second data structure 4. Step 203 of the formation of the second data structure 4 is characterized by the execution of logic construction step 2031, in which logical structures 41 of logical partitions 111, 112 of elements 31 are formed , 32, 33, 34 of the first data structure 2, using information from the database 3 of logical connections of the logical partitions of the elements 31, 32, 33, 34 of the first data structure 2 and the logical partitions of the mentioned elements 31 of the first page data structures 2 containing one of said first logical partitions 111 and logical partitions of said elements 32 of a first data structure 2 containing one of said second logical partitions 112; and performing step 2032 of generating a second data structure 4, wherein a second data structure 4 is formed comprising elements 41 of the second data structure 4, said elements of the second data structure 4 being formed logical structures 41 of logical sections 111, 112 of elements 31, 32, 33 , 34 of the first data structure 2. Logical constructions 41 are the result of transforming the data of the converted structured data array. Logical constructions 41 are formed in accordance with the specifics of the converted text in natural language, in particular, normative documents. The specificity of the legal acts is that it contains the rule of law (legal norms). Also, the specificity of the legal acts is that in the theory of the rule of law there are concepts of a logical rule of law and a legal rule of law. These concepts are not identical. The difference lies in the fact that the logical rule of law includes the content of all elements of the rule of law established in legal science, including hypothesis, disposition and sanction, and the legal rule of law reflects the specific regulatory requirements contained in specific proposals of specific legal acts. In fact, the difference lies in the fact that one particular logical rule of law can be contained in a specific set of legal rules of law, i.e. in a multitude of regulatory requirements. The logical design is the basis (framework) of the main regulatory requirement, containing two basic structural elements - “situation” and “rule”. The main regulatory requirement (hereinafter referred to as the regulatory requirement) is an instrument of legal regulation and includes regulatory and protective regulatory requirements. In this case, the situation in the regulatory requirements means any conditionality of the rule, and the rule means any rules, including the rules (model) of behavior of subjects of legal relations. In other words, a situation is a judgment having a logical implicative connection and being a basis, and a rule is a judgment having a logical implicative connection and being a consequence. In the formation of logical structures 41, i.e. It is also necessary to take into account regulatory requirements that each of the elements of this design (both the situation and the rule) can consist of both a single judgment and a group of judgments. For the formation of logical constructions, it is necessary to use the database of logical connections of logical partitions. In addition to the identified logical connections between logical sections for the formation of a logical structure, you must refer to the rules for the formation of logical structures. The rules for the formation of logical constructions reflect the requirements of legal science and legal practice in relation to the composition and structure of a normative prescription (prescription). For example, the condition that one prescription cannot contain two different rules leads to the fact that the rules establish that if one sentence contains two consequences that have a logically weak disjunctive connection, this means that these judgments are different rules and accordingly different regulations. Moreover, if the same two consequences have a logically strong disjunctive connection, then this unites them in the framework of one complex rule within the framework of one prescription. In essence, the rules for the formation of logical constructions 41 are reduced to permissible combinations of logical connections between the same type of judgments within the framework of a single normative prescription.

Moreover, if a sentence contains several situations united by the logic “OR” (weak disjunctive connection), then this means that each of these situations forms its own separate prescriptions with the same rules that were used in the first prescription. This means that several situations with such “OR” logic cannot be in the same prescription. In addition, the proposal has several rules, united by the logic “OR” (weak disjunctive connection). This means that each of these rules forms its own separate prescriptions with the same situations that were used in the first prescription. This means that several “rules” with such “OR” logic cannot be in one prescription. The second data structure formed in the above manner contains such elements as judgments (logical section of the element of the original data structure) and regulatory requirements (logical structure 41 of logical sections of the element of the original data structure) (Fig. 8). In this case, judgments are identified by the presence of an implicative logical connection into two main logical sections:

1) foundations (the first logical section of the element of the original data structure containing a logical implicative connection, a connection of the 1st type, type A);

2) consequences (the second logical section of the element of the original data structure, containing a logical implicative connection, a connection of the 1st type, type B);

In this case, the grounds and consequences are additionally identified also by the fact of identifying other logical connections between the same implicative judgments within the framework of one sentence as additional logical sections:

1) judgments “AND” (the logical section of the element of the original data structure containing the logical conjunctive (connecting) connection, the connection of the 2nd type);

2) “OR” judgments (the logical section of the element of the original data structure containing the logical weak disjunctive (dividing) connection, type 3 communication);

3) “OR *” judgments (a logical section of an element of the original data structure containing a logical strong disjunctive (separation) connection, a type 4 connection);

In addition, the above sections can be separately identified as “denied judgments” (a logical section of an element of the original data structure containing a denied logical link, a type 5 link).

In FIG. 9 as an example, but not limitation, depicts a General diagram of the steps of step 204 of the formation of the database 5 semantic parts. Stage 204 of the formation of the database of 5 semantic parts is characterized by the execution of step 2041 identification of logical partitions, which identify the first logical partitions 411 elements 41 of the second data structure 4 and the second logical partitions 412 elements of the second data structure 4; by performing a semantic part identification step 2042, wherein in the first logical partitions 411 and second logical partitions 412 of the elements of the second data structure 4, the first semantic parts 4110 and the second semantic parts 4120 are identified; and by performing step 2043 of identifying specific semantic parts, in which at least the specific semantic parts 4111 of the first logical sections 411 of elements 41 of the second data structure 4 and special semantic are identified in the first and second logical sections 411, 412 of the elements 41 of the second data structure parts 4121 of the second logical partitions 412 of the elements 41 of the second data structure 4 and form a database 5 of special semantic parts of the logical partitions of the elements 41 of the second data structure 4 by moving the of the general semantic parts 4111, 4121 to said forming database 5 of special semantic parts of logical sections of the elements 41 of the second data structure 4 (FIG. 10). The logical structures 41 formed in the second data structure are the framework and the basis of the normative prescription, but still do not fully comply with it. To achieve maximum compliance with the structure of logical constructions 41 to the structure of a normative prescription, it is necessary to conduct a comprehensive semantic analysis of logical sections 411, 412, including at least syntactic and logical analysis of terms and concepts, identifying the relationships between concepts of judgment and between terms of complex concepts. The purpose of this semantic analysis is to identify and identify in logical sections 411, 412 logical structures 41 of the second data structure 4 rows of specific parts (second parts) of logical sections that lead to:

1) a mixture of the basic concepts of a legal norm - to a mixture of situations and rules by including various conditions in the judgment;

2) blurring - defocusing the meaning of judgments by including various qualitative and quantitative refinements and details into the judgment.

At this stage, the identification of specific parts, i.e. the identification in logical sections 411, 412 of logical structures 41 of the second data structure of the first and second semantic parts 4110, 4120 of logical sections 411, 412. Moreover, the first semantic parts! 110 are formed by removing from the logical sections 411, 412 second semantic parts 4120 (specific parts) . The first semantic part 4110 of the logical section is the semantic core of judgment, i.e. judgment cleared of specific parts. The semantic core of judgment is the basic elements of judgment, such as the subject of the judgment, the predicate of the proposition, and the connective. The peculiarity of the connective is that the connective is only part of the semantic core of the judgment when it cannot be interpreted in a “three-dimensional plan”, in cases where the connective discloses the inclusion (or exclusion) of a subclass in the class of objects or the belonging (non-affiliation) of an element to the class . For example, in the judgment: “Crime is an unlawful act”, the subject of the judgment is the word “crime”, the predicate of the judgment is the phrase “unlawful act”, and the link is the word “is”. The second semantic parts of the 4120 logical section are the concepts of judgment, which are identified as signs of the subject of the judgment, the predicate of judgment, as well as the terms of the judgment — a connective (when it can be interpreted in the “three-dimensional plane”) and a quantifier word, as well as other, special parts. For example, the concept (subject of judgment) “crime provided for by the Criminal Code” contains the concept - the word “crime” and the sign of the concept - the phrase “provided for by the Criminal Code”. Signs of a concept - this is the content of a concept indicating the presence or absence of one or another property, state or relationship. In other words, a sign of a concept is all that in which the concepts can be similar or different from each other. All signs of a concept that form the content of concepts are identified as significant and non-essential by the principle of loss of one's quality (inability to be oneself) without this attribute. For example, in a judgment: “Crime is an unlawful act”, the predicate of a judgment is the concept of “unlawful act” (B), which is a complex concept or “attitude” in which “act” is the subject of judgment (A), and the concept of “unlawful” - is a sign of A. On the example of “relationship”, “illegal act” shows that it contains “concepts” in which the volume of one is fully included in the volume of the other, but does not exhaust it. In other words, all elements of volume (B) are elements of volume (A), but not vice versa. The type of such relations is “submission”, i.e. generic relation, where the more general “concept” acts as a genus, and the less general - as a species. A quantifier word indicates whether information about a predicate of a judgment applies to the whole volume of a concept expressing a subject of a judgment, or to a part of it. For example, in the judgment: “Every crime is an unlawful act”, the quantifier word “any” indicates that information about the subject of the judgment (the phrase “unlawful act”) refers to the entire volume (to each element of the volume) of the subject of the judgment - the word “crime” . Other, special parts are understood to mean such separate concepts and groups of concepts of judgment, which also clarify the meaning of concepts that make up the first semantic part 4110 of logical sections 41, but do not formally relate to the features of a concept, a bunch, or a quantifier word. For example, in the judgment: “Crime (including fraud, theft, murder) is an unlawful act”, the terms indicated in parentheses (“fraud”, “theft”, “murder”) are real (life) examples concepts of "crime". The sign of the concept of “unlawful” is essential for the concept of “act”, since without it the concept of “unlawful act” loses its quality, ceases to be itself. The scope of a concept is a class (set) of entities conceivable in a concept. The signs of the concept and the scope of the concept are interconnected within each concept. This relationship allows us to establish the real scope of the concept, i.e. what is really implied in the semantic content of the concept. In order to achieve maximum conformity of the structure of logical constructions 41 to the structure of the normative prescription, it is necessary to separate the types of the second semantic parts 4120. From a technical point of view, other, special parts identified in the process of complex semantic analysis are not an element of the main (regulatory or protective) normative prescription. In this regard, they are removed from the logical sections and form a database of 5 special semantic parts, which is a regulatory reference or other regulatory reference material, which represents a lot of special regulatory requirements. Information from such a reference is accessible and relevant, but structurally and methodologically it is beyond the scope of the basic (regulatory and protective) regulatory requirements (Fig. 10). From the point of view of legal science, special regulatory prescriptions are prescriptions that establish the basic principles, mechanisms, order and goals of the legal regulation of public relations, consolidate legal categories and concepts (for example, definitive prescriptions - prescriptions that consolidate in a generalized form the attributes of a legal concept) .

In FIG. 11, by way of example, but not limitation, a general flowchart is shown of the steps of step 205 of forming grammatically and spelling-correct semantic parts, on which grammatically and spelling-correct semantic parts of said logical sections 41 are formed by linguistic transformations over said semantic parts. Step 205 includes performing a refinement structure identification step 2051, wherein at least second refinement structures of the second semantic 4122 parts of the second logical partitions 412 are identified in said second semantic parts 4120 of said second logical partitions 412 of elements of the second data structure 4; and performing a step 2052 of linguistic transformations, where linguistic transformations are performed on all semantic parts, with the exception of the mentioned special semantic parts 4111, 4121 of the first and second logical sections 411, 412, to form grammatically and spelling-correct semantic parts 4123 and qualifying logical structures 4122 sections of elements 41 of the second data structure 4. A general diagram of the obtained second data structure 4 is shown in FIG. 12. To achieve maximum compliance with the structure of logical constructions 41 to the structure of a normative prescription, it is necessary to additionally identify the remaining types of second semantic parts. Additional identification is also carried out as part of a comprehensive semantic analysis. The subject of analysis will be an array of values identified by the indicated types of the second semantic parts 4120, i.e. an array of concepts contained in logical sections and identified as corresponding species. Each concept of these arrays should be identified from the point of view of its belonging to refinements 4124 or to dependencies 4125. The refinement is such a characteristic of the concept that carries out the transition from a broader concept to a narrower one, and the dependencies contain signs of a legal fact, i.e. a certain event, in the presence (absence) of which the concept to which the dependence refers is actualized, or vice versa becomes irrelevant. Linguistic transformations over all semantic parts of logical sections are associated with the restoration of the correct grammar and spelling of individual semantic parts, which will be required in connection with the actual division of the text of sentences into separate parts - semantic parts 4110, 4120 of logical sections, and taking into account the removal of special semantic sections 4111, 4121 from the specified text. By the indicated linguistic transformations we mean, in particular, the coordination of childbirth, numbers, cases, editing (replacement and removal) of inappropriate punctuation marks.

In FIG. 13, by way of example, but not limitation, a general flowchart of the steps of step 206 of generating the final data structure 6 is shown, in which the resulting data structure 6 of the structured source data array is formed comprising the elements 61 of the resulting resultant data structure 6, the aforementioned elements 61 of the final data structure contain logical constructions 61 containing at least the grammatically and spelling-correct semantic parts 4123 of logical sections. Step 206 is characterized by the execution of a semantic combination step 2061, in which 4123 second logical partitions 4 of the second data structure 4 and 4 grammatical and spelling correcting structures 4122 of the second semantic parts 4120 of the second logical partitions 412 of the elements are formed from the first grammatically and spelling-correct semantic parts 4123 41 second data structures 4 semantic combinations 611 grammatically and spelling-correct semantic parts 4122, 4123 second logical sections 412 element s 41 of the second data structure 4; and by performing step 2062 of generating the resulting data structure 6, wherein the resulting data structure 6 is formed comprising the elements 61 of the resulting data structure 6, said elements 61 of the resulting data structure 6 being logical structures 61 containing the grammatically and spelling-correct semantic parts 4122, 4123 logical partitions of the elements 41 of the second data structure 4. A situation is also possible when the logical structures 61 of the resulting data structure are given x 6 additionally contain the mentioned generated semantic combinations 611 of grammatically and spelling correct semantic parts 4122, 4123 of the second logical sections 412 of the elements 41 of the second data structure 4 (Fig. 14). The final overall structure of element 61 of the resulting data structure 6 is shown in FIG. 15. The basis of a normative prescription is a legal rule (rule), which can be formed as correctly as possible as a result of a comprehensive semantic analysis of logical sections of logical structures. At the stage of formation of logical constructions, most of the conditions were separated from the rule (second logical section 412) and separated into a separate section - the first logical section 411. Based on the results of complex semantic analysis, the semantic core of the rule (second logical section 412) was revealed and the rest of the conditions were 4120, in which some of the indicated parts are identified as refinement 4124, are highlighted in the second semantic parts. As a result of all the transformations, it became possible in the logical structure FIR 61 structures create a semantic coupling 611, i.e. combinations of the first semantic parts 4110 of the second logical sections 412 and the second semantic parts 4120 of the second logical sections 412, identified as refinement 4124. These semantic combinations are legal rules. The resulting data structure is a structured structure, the elements of which are most consistent with the structure of the regulatory prescription. The resulting data structure is formed in this form in order to simplify and regulate the professional work on the creation and adjustment of regulatory documents. The resulting data structure is a design that allows you to literally visualize regulatory requirements, see all the actual elements of the semantic design, which allows them to be comprehensively analyzed in order to make precise adjustments to both existing regulatory requirements and draft regulations at different stages of their creation.

In FIG. 16 as an example, but not limitation, depicts a General diagram of the steps of the claimed method 300 of forming a map of the relations of the components of the grammatically and spelling correct semantic parts of the logical sections of the logical structures of the converted structured source data array. The claimed method 300 for generating a map of component relationships is characterized by performing a step 301 of identifying components and values, which identify the components of the grammatically and spelling correct semantic parts of logical sections of logical constructions from said transformed structured source data array, each grammatically and spelling-correct semantic part containing said components, contains at least one of the aforementioned component, moreover, yanuty component comprises no more than one component value, and generating a table of components comprising at least said identified components and their values; step 302 of identification of semantic parts, which identify grammatically and spelling correct semantic parts of logical sections of logical structures containing said identified components, and grammatically and spelling correct semantic parts of logical sections of logical structures that do not contain said identified components; component identification step 303, in which components containing more than one in said grammatically and spelling correct semantic parts are identified and combinations of components contained in more than one in each separate grammatically and spelling correct semantic parts are identified, and a map of component combinations containing at least the aforementioned components contained in an amount of more than one in each separate grammatically and ortho graphically true semantic part, and their combination; step 304 identifying the values of the components, which identify the values of the components contained in the aforementioned map of combinations of components; concept identification step 305, in which, in the grammatically and spelling-correct semantic parts of the logical sections of logical constructions that do not contain the identified components identified, concepts are identified that are semantically the same as the values of the components contained in the component combination map, and a table of semantically matching concepts containing at least the values of the components contained in the said map of combinations of components and semantically matching these concepts; step 306 of identifying semantic parts that do not contain components and containing a concept, which identifies at least one grammatically and spelling-correct semantic part of a logical section of a logical construction that does not contain the identified components and contains more than one of the aforementioned concepts; stage 307 identification of semantic parts containing components whose values coincide with the concepts, identify the grammatically and spelling-correct semantic part of the logical section of the logical construction, containing more than one identified component, the values of which semantically coincide with the above-mentioned concepts, contained in one grammatically and spelling-correct semantic parts of a logical partition of a logical construct that does not contain the identified identified computers nents and comprising more than one of said concept; step 308 of forming a map of combinations of semantically matching concepts, which form a map of combinations of semantically matching concepts, containing at least the mentioned concepts, containing more than one in one grammatically and spelling-correct semantic part of the logical section of the logical construction that does not contain the identified components semantically coinciding with the values of the identified identified components contained in one grammatically and spelling in the black semantic part of the logical section of the logical structure containing more than one of the identified components; and step 309 of forming a map of component relationships, where a map of component relationships of grammatically and orthographically correct semantic parts of logical sections of the logical constructs of the transformed structured source data array containing at least said combination map of components and said map of combinations of semantically matching concepts is formed. Additionally, the method 300 of generating a map of the connections of the components may include a step 310 of generating a report on which a report is generated showing at least the number of links and the values of said components contained in the map of combinations of components. A component is an attribute of a legal rule. A legal rule is part of a regulatory enactment. The purpose of the component structuring of rules is the formation of a certain design standard and systematic technical assessment of the construction of a legal rule, as well as the search and identification of related requirements that form the construction of a logical rule of law. All components of the rule mentioned in the present description are examples used for the purpose of explaining the present invention. Other components without restrictions may be identified in accordance with this method in other legal rules. The first component of a legal rule, as an example, but not a limitation, is the subjects of legal relations - these are authorized persons established in the precept, the relations between which are governed by the precept. In addition, one of the parties may be used in the order for which a certain rule is established. Parties to legal relations are referred to as the subject of the order and the counter-subject. In this case, the subject of the prescription is the cause and source of regulatory influence, and the counter-subject is the party to the legal relationship with which this effect is associated. The second component of a legal rule, as an example, but not a limitation, is the regulatory impact - an action, inaction, requirement, principle, etc. If the prescription of the subject of legal relations, the regulatory impact will be the content of the legal relationship - i.e. the possibility of certain actions, the need for certain actions or the need to refrain from prohibited actions. If the order does not contain the subject of legal relations, then the regulatory impact will be the content of the requirements relating to the objects of legal relations. The third component of a legal rule, as an example, but not a limitation, is the object of legal relations - these are objects, phenomena and their derivatives (hereinafter referred to as objects), these are objects of the world around which the regulatory influence is directed. If the rule does not contain the subject of legal relations, then the objects of legal relations are those objects that are subject to regulatory influence in the form of the content of requirements for these objects. The fourth component of a legal rule as an example, but not limitation, is related objects - these are objects, phenomena and their derivatives that are present in the rule, but are not objects of legal relations. Related objects are used in the rule either to detail and clarify the regulatory impact, or as an object of regulatory impact, or as an object of regulation.

Each component (type of component) installed in the component structure (see table 5) is described using various linguistic tools, such as syntactic functions and relationships, parts of speech, sentence members, etc. Linguistic tools describe individual terms (concepts) and groups of terms (concepts) included in the semantic parts. Such descriptions are formed in the form of logical formulas in which the elements are the mentioned linguistic tools connected with each other by logical connectives - conjunction ("and"), disjunction ("or"), strict disjunction ("either, ... or"), implication ( "If ... then"). Logical formulas for each component (type of component) form a database of descriptions of components. Component descriptions database is a set of tools for finding components in grammatically and spelling-correct semantic parts of logical sections of logical constructions of a transformed structured source data array. The map of component relationships in this case consists of internal and external component relationships. The internal relationships of components indicate combinations of components within one rule (within the semantic part containing components). External relations of components indicate the relationship between individual regulatory requirements based on the presence of semantic relations between components and individual words, and phrases in semantic parts that do not contain components. To identify components in the grammatically and spelling-correct semantic parts of logical sections of logical constructions from the aforementioned transformed structured source data array, it is necessary to first identify among all semantic parts only those semantically parts that contain components. This is the first semantic part of the second logical section of the logical construction. After identifying the semantic parts containing the components, using the tools to search for the components contained in the component descriptions database, the components themselves are identified. By the identification of components is meant the identification in each separate semantic part containing the components of the following information: a) the name of the component; b) the type of component; c) the value of the component. The name of the component is an element of a legal rule, reflecting the signs of legal relations in a particular rule, which is the main part of the linguistic formula. The type of component is the main and additional parts of the linguistic formula, i.e. separate subgroups of component names identified on the basis of differences in the additional part of the linguistic formula. The meaning of a component is a term or group of terms contained in the semantic part that correspond to the main and additional parts of the linguistic formula. As an example, but not limitation, the components will be identified in the legal rule:

“The authorized body is obliged to take measures to recall such goods from the domestic market from the consumer.” In this example, all five components of the rule are identified (see table 6).

In a legal rule, the semantic core of which corresponds to the first semantic part of the second logical section, each component can have one value. Based on the results of the identification of components in the first semantic parts of the second logical section of the logical structure, a table of components is formed. The table of components contains the following information: name of the component; type of component; component value unique semantic part number; Unique logical partition number unique logical design number. Based on the information contained in the component table, semantic parts containing more than one component and semantic parts containing one component are identified from the array of semantic parts containing components. The reason for this separation of semantic parts containing components is that the component combination map is an array of actual component combinations in each individual rule, i.e. in each separate semantic part containing the components. Since any combination requires at least two elements, a map of component combinations can be formed on the basis of semantic parts containing more than one component. Moreover, each semantic part containing more than one component forms a group of components of one rule. Component combinations within each individual component group form a map of component combinations. The component combination map contains the following information: name of the combined components; types of components to be combined; the value of the combined components; unique semantic part number; Unique logical partition number unique logical design number. As an example, but not limitation, maps of combinations of components in a legal rule will be produced:

“The authorized body is obliged to take measures to recall such goods from the domestic market from the consumer.”

The combination of components and their types indicated in the example allows displaying completely or partially matching prescription groups in the report. Full coincidence implies the presence of prescriptions in the sample that have the same set of components or types of components or component values. Partial coincidence implies the presence in the sample of prescriptions of matching combinations of individual components, or types of components, or values of components. As an example of complete coincidence, we can cite a selection of requirements in which all the requirements contain, for example, the same composition of components as the composition of the components shown in table 7, namely K1-K1-K2-K3-K4, or the composition indicated there types of components, or specified values. An example of an incomplete coincidence is a selection of prescriptions in which all prescriptions contain a part of the components specified by the user from the set combination - for example, only combination K1-K3. Such a selection includes not only the requirements with the established combination of K1-K3, but also with other combinations of components (for example K1-K2-K3), which contain other components (for example, K2) along with the components indicated in the combination K1-K3. The component combination map is the first part of the component association map. The external relations of the components that make up the second part of the map of component relationships are based on the presence of semantic relationships between components and individual words, and phrases in semantic parts that do not contain components. The semantic connection is understood as the presence between the meanings of the components and individual words, and phrases of semantic coincidence, i.e. coincidence of the meanings of the individual components and words within the meaning. As an example, but not limitation, one rule can be considered (the semantic part containing the components) - “The seller must transfer the goods to the consumer”, and one semantic part not containing the components (situation, legal fact) - “If the goods are not transferred to the consumer. " First of all, it is necessary to identify the external relations of the components, i.e. the presence of a semantic connection between the meanings of components and concepts that semantically coincide with them. As a result, for this example, three relationships will be revealed between the following meanings of the components and the words in the semantic part “situation”: a) product - product; b) must transfer - not transferred; c) to the consumer - to the consumer. To identify the second part of the component association map, only the components registered in the component combination map are used - i.e. components contained in one legal rule in an amount of more than one. At the same time, in the semantic part that does not contain components (in situations), in which concepts are identified that semantically coincide with the meanings of the components mentioned, at least two such concepts should be revealed. In this case, semantically coinciding concepts can be not only completely matching or one-root words (terms), but also their synonyms and associations identified in special connected dictionaries and reference books. All concepts that semantically coincide with the values of the components identified in this way are recorded in a special table - a map of combinations of semantically coinciding concepts. A map of combinations of semantically coinciding concepts establishes relationships between individual semantic parts containing components and semantic parts that do not contain components. This relationship is based on identified relationships between components and related concepts. As a result, related requirements are identified. A map of combinations of semantically matching concepts contains the following information for each pair of related prescriptions: name of components; types of components to be combined; the value of the combined components; unique number of the semantic part containing the components; unique number of the logical section containing the mentioned semantic part; a unique logical construction number containing the logical partition; the name of concepts that semantically coincide with the meanings of the mentioned components; unique number of the semantic part containing the mentioned concepts; unique number of the logical section containing the mentioned semantic part with concepts; a unique number of the logical construct containing the mentioned logical section and the mentioned semantic part with concepts. The map of component relationships consists of two parts - from a map of combinations of components and a map of combinations of semantically identical concepts. A combination of components map can be used in a comprehensive legal analysis of some legal content, for example, an industry or thematic group of legal acts, or a separate legal act or a separate draft legal act. According to the information registered in the Component Combination Map, you can instantly get various “system-structural sections” of the legal acts (draft legal acts or group of legal acts) reflecting at least: the component structure of the rules (both at the level of the names used in the legal acts of the components and at the level types and values of components); the presence of separate combinations of components in the rules of the regulatory legal acts (also at three levels - name, type and value). The first part of the communication map can be generated and presented in the form of a report. This report can be generated according to the scheme and format, which will be set by the user in advance and will contain selective and system-selected information clarifying the sections of the regulatory acts that are important for the user (draft legal acts, group of legal acts). A map of combinations of semantically coinciding concepts can be used both in the legal analysis of certain legal content, for example, an industry or thematic group of legal acts, or a separate legal act or a separate draft legal act, and to search for related provisions in any legal content. The simplest practical result of identifying relationships between prescriptions is to solve the problem of automatically identifying prescriptions establishing sanctions for violation of a rule specified in the original prescription and vice versa - to identify the relationship of the original prescription with the rule on the basis of the prescription containing sanctions for violation of this rule. The related prescriptions identified in this way form a rule of law in which there are regulatory and protective parts.

In FIG. 17, by way of example, but not limitation, a general flowchart of the inventive method 400 for finding relevant information in a transformed structured source data array containing at least logical constructs containing grammatically and spelling-correct semantic parts of logical sections of logical constructs is shown. The claimed method 400 for searching relevant information is characterized by performing step 401 of generating a search query, which form a search query containing at least one search term and, if necessary, additionally assign at least one search criterion, which may be one from: search in the first and second semantic parts of the first logical sections of logical structures and the second semantic parts of the second logical sections of logical structures; search in the first semantic parts of the second logical sections of logical constructions and the second semantic parts of the second logical sections of logical constructions; wherein the search query may be one of: at least one of: terminological search query; component search query; component terminological search query; a search array identification step 402, which identifies a data array in said transformed structured source data array corresponding to said search term; step 403 of the identification of semantic parts, which identify at least one grammatically and spelling correct semantic part of the logical sections of the logical structures of said transformed structured source data array containing at least said search term; a logical structure identification step 404, wherein at least one logical structure of said transformed structured source data array is identified, comprising at least said grammatically and spelling-correct semantic part; and step 405 of demonstrating semantic parts, which demonstrate at least said identified logical construction of said transformed structured source data array containing at least said identified grammatically and spelling-correct semantic part, which is the sought-for relevant information. Additionally (Fig. 18), the claimed method 400 for searching for relevant information can be supplemented by a second search level, which consists in performing step 406 of selecting a logical structure, which selects at least one of these identified logical structures containing at least the aforementioned the grammatically and spelling-identified semantic part identified; step 407 of identifying related semantic parts, which identify at least grammatically and spelling correct semantic parts associated with said grammatically and spelling correct semantic parts contained in other logical constructs of the transformed structured data array, said identification being carried out on the basis of information from the map relations of components of grammatically and spelling-correct semantic parts of logical sections of logical constructs uktion; a logical construct identification step 408, wherein at least one or more logical constructs containing said identified grammatically and spelling correct semantic parts are identified or the absence of logical constructs containing said identified grammatically and spelling correct semantic parts is identified; and step 409 of demonstrating the logical constructs, which demonstrate at least one or more logical constructs containing the identified identified grammatically and spelling correct semantic parts or demonstrate the absence of logical constructs containing the identified identified grammatically and spelling correct semantic parts. Initially, the search array is a transformed structured source data array - i.e. normative legal acts in the form of logical constructions consisting of many data sub-arrays in which interdependencies exist both at the level of sub-arrays and between individual values of sub-arrays. Sub-arrays of the transformed structured source data array are grammatically and spelling-correct semantic parts of logical sections of logical constructions. Relevance of a search is the identification in the text of a normative legal act of logical constructions, which are normative prescriptions that correspond, at least, to the search query. To achieve the desired level of search relevance, one of the possible search criteria that are intended to refine the search area can be additionally assigned. To achieve the first level of search relevance, a search criterion is assigned to the first and second semantic parts of the first logical sections of logical constructions and the second semantic parts of the second logical sections of logical constructions. This search criterion allows you to search in arrays related to sections of situations. By assigning this search criterion, it will be possible to select the appropriate subarray (situations subarray) in which relevant information will be searched in accordance with the search query. A search query using this search criterion can only be terminological. A terminological query is a search query for matches with the term (concept) set in the query in the subarrays that match the search criteria. To achieve the second level of search relevance, a search criterion can be applied on the first semantic parts of the second logical sections of logical structures and the second semantic parts of the second logical sections of logical structures. This search criterion allows you to search in arrays related to rule sections. By assigning this search criterion, it will be possible to select the appropriate subarray (rule subarray) in which relevant information will be searched in accordance with the search query. A search query can be simple and complex. Simple queries are a terminology query and a component query. A component request is a request to search for matches, both by the presence of the components of a legal rule, and by their types, as well as by their values. A component request can be three-level - search by the name of a component of a legal rule (first level of a component request), search by type of a component of a legal rule (second level of a component request), value of the type of component (third level of a component request). Moreover, the first two levels are a search by the structure of the components of a legal rule. The third level is a search by terms from the text of the LA, indexed in the corresponding subarrays as the value of the type of component of the legal rule. A complex search query is a combination of two simple queries - component and terminological. A component-terminological query is a component query, supplemented by a fourth search level - a search by term or terms contained in the legal rules identified by the results of the component query. At the same time, performing a search query can be multi-stage. In particular, at the first stage of the search, the search criteria and the search query are applied to the entire base of legal entities. In the second and subsequent stages, the application of search criteria and search queries to the search results obtained by performing the first search stage can be carried out. After assigning a search criterion and executing a search query, in the subarrays of the transformed structured source data array that correspond to the search criterion, matches of indices and values with the set values of the search query are revealed. The result of the search query is the identification of a list of individual values of subarrays that correspond to the values of the search query. The values of subarrays identified as a result of the analysis of the transformed structured source array in accordance with the search criteria and the search query are associated with those semantic parts of the logical sections of the logical constructions in which they are registered. As a result, those logical constructions are identified that contain semantic parts in which the values corresponding to the search query in subarrays matching the search criteria are identified. As a result of the search for relevant information in the transformed structured source data array, the identified logical constructions - regulatory requirements will be demonstrated. The format of logical constructions corresponds to the format of elements of a transformed structured source data array and contains a legal rule and conditionality of the rule (conditionally - “situation”). Moreover, the legal rule consists of the semantic core of the legal rule and the refinement of the concepts of the semantic core of the rule, and the conditionality of the rule consists of the semantic core of the situation, the refinement of the concepts of the semantic core of the situation, the dependencies of the concepts of the semantic core of the situation and the dependencies of the concepts of the semantic core of the rule. Accordingly, in the case of assigning more than one value of the search query (i.e., generating several queries at the same time), it is additionally necessary to identify the logical relationship between the assigned values. The identification of a logical connection is necessary to assign an additional search criterion, which allows matching several search queries and the correct identification of the corresponding logical structures. In this case, several search queries can be coordinated by the logical connection “and” (conjunction) or the logical connection or ”(disjunction). The purpose of conjunctive logical communication allows you to identify regulatory requirements that are fully consistent with all requests, i.e. regulatory requirements, which contain in the relevant search criteria parts of the text of the regulatory requirement, both the meaning of the first request and the values of the others associated with the first request. The purpose of a disjunctive logical connection allows you to identify regulatory requirements that are fully consistent only with individual search queries, i.e. regulatory requirements, which contain in the relevant search criteria parts of the text of the regulatory requirement either the meaning of the first query or the meaning of other queries. Further, the search method may be supplemented by a second level of searching for relevant information. To implement this search method, the result of which is the identification of a logical rule of law, it is necessary to select one of the identified logical structures (one normative prescription), which will be the starting point for identifying the entire set of logical structures associated with the selected logical structure that form the logical rule of law. The identification of the relations of the semantic part of the selected logical construction with the semantic parts of this and other logical constructions is based on information from the second part of the component relationship map (from the map of combinations of semantically identical concepts). The minimal task of identifying a logical rule of law is the task of identifying a normative prescription that establishes sanctions for violating a legal rule specified in the original logical construction (in the original normative prescription) or vice versa - identifying the normative prescription on the basis of the original logical construction containing sanctions. In the form of a search query, either component values or concepts (terms) that semantically coincide with the component values can be used. It depends on what task is set when identifying the logical rule of law and this determines which arrays will be searched. When searching for sanctions for violation of a legal rule, the search query is the values of the components of the rule, and the search array is the arrays of all semantic parts related to the conditions of the rule. When searching for the legal rule itself by the known, previously identified, sanctions, the search query are terms that semantically coincide with the values of the rule components, and the search array is the arrays of all semantic parts related to the legal rule. As a result of a two-level search for relevant information in a transformed structured source data array, logical constructs are identified that contain semantic parts that contain terms or components that correspond to the second level search query and which are associated with the original logical construct within the framework of the logical rule of law. To identify such relations, external relations of components are used, which make up the second part of the map of component relations (a map of combinations of semantically identical concepts), based on the presence of semantic relations between components and individual words, and phrases in semantic parts that do not contain components. The semantic connection is understood as the presence between the meanings of the components and individual words, and phrases of semantic (semantic) coincidence, i.e. coincidence of the meaning of individual components and individual words within the meaning. As an example, but not limitation, we can consider one legal rule (the semantic part containing the components) - “The seller must transfer the goods to the consumer” and one semantic part not containing the components (conditionality of the rule, situation, legal fact) - “If the product is not transferred to the consumer. " The identification of the external relations of the components, i.e. the presence of a semantic connection between the meanings of the components and the concepts that semantically coincide with them. As a result, for this example, three relationships between the following meanings of the components and the words in the semantic part “situation” were revealed: a) product - product; b) must transfer - not transferred; c) to the consumer - to the consumer. To identify related semantic parts, only components registered in the component relationships map are used - i.e. components contained in one legal rule in an amount of more than one. Moreover, in the semantic part that does not contain components (“situations”), in which concepts are identified that semantically coincide with the meanings of the components mentioned, at least two such concepts should be revealed that semantically coincide with at least two meanings of the components from one legal rule. In this case, semantically coinciding concepts can be not only completely matching or one-root words (terms), but also their synonyms and associations identified in special connected dictionaries and reference books. All concepts that semantically coincide with the values of the components identified in this way are recorded in a special table - a map of combinations of semantically identical concepts. A map of combinations of semantically coinciding concepts establishes relationships between individual semantic parts containing components and semantic parts that do not contain components. This relationship is based on identified relationships between components and related concepts. As a result, related requirements are identified. A map of combinations of semantically matching concepts contains the following information for each pair of related prescriptions: name of components; types of components to be combined; the value of the combined components; unique number of the semantic part containing the components; unique number of the logical section containing the mentioned semantic part; a unique logical construction number containing the logical partition; the name of concepts that semantically coincide with the meanings of the mentioned components; unique number of the semantic part containing the mentioned concepts; unique number of the logical section containing the mentioned semantic part with concepts; a unique number of the logical construct containing the mentioned logical section and the mentioned semantic part with concepts. A map of combinations of semantically coinciding concepts can be used both in the legal analysis of certain legal content, for example, an industry or thematic group of legal acts, or a separate legal regulation or a separate draft legal regulation, and to search for related requirements at the request of the user in any legal content. As a result of identification, the user is shown the results in the form of identified related requirements (separate legal norms).

In FIG. 19, by way of example, but not limitation, an exemplary diagram of the inventive system 500 is illustrated, which in a preferred embodiment includes at least one or more devices 501 comprising at least one or more processors 5011, one or more input modules I / O 5012 and memory 5013. The devices 501 mentioned may include, but are not limited to: a personal computer, a laptop computer, a tablet computer, a PDA, a smartphone, a thin client, and the like. The memory (computer-readable storage medium) 5013 of the device 501 contains a program code which, when executed, causes said one or more processors 5011 of said device 501 and / or device 501 associated with it to perform the steps described in Embodiments 1 through 12 of twentieth to twenty-first embodiments of the present invention, and twenty-ninth to thirty-fourth embodiments of the present invention, and comprises to be converted or transformed one or more structured source data arrays containing at least natural language text. Moreover, to be converted or converted one or more structured source data arrays can be downloaded and stored, in particular, in the database 502 of the structured data array conversion system. By way of example, but not limitation, a computer-readable storage medium may include random access memory (RAM); read-only memory device (ROM); Electrically Erasable Programmable Read-Only Memory (EEPROM); flash memory or other memory technologies; CDROM, digital versatile disc (DVD) or other optical or holographic storage media; magnetic cassettes, magnetic tape, magnetic disk storage device or other magnetic memory devices, wave carriers or other storage medium that can be used to encode the required information, and which can be accessed through the described device. The memory includes a storage medium based on a computer storage device in the form of volatile or non-volatile memory, or a combination thereof. Exemplary hardware devices include solid state memory, hard disk drives, optical disk drives, etc. An example environment is stored in the memory in which, using computer instructions or codes stored in the device’s memory, the procedures for pre-converting a structured source data array, the procedures for converting a structured source data array, forming a map of the components of the converted structured source data array, and the procedure can be performed search for relevant information in transformed structured source data arrays. The device contains one or more processors 5011, which are designed to execute computer instructions or codes stored in the device memory in order to ensure that the above procedures are performed. Computer instructions or codes stored in memory are designed to perform preliminary transformations of a structured source data array, transformations of a structured source data array, form a map of connections between components of converted structured source data arrays, and search for relevant information in converted structured source data arrays. The I / O 5012 modules of device 501 are, but are not limited to, typical and prior art device controls: a mouse, keyboard, joystick, touchpad, trackball, electronic pen, stylus, touch screen, and the like. Also, I / O 5012 modules are, but are not limited to, typical and known from the prior art means of displaying information: a display, a monitor, a projector, a printer, a plotter, and the like. The system 500 may also include a database (DB) 502. The database 502 may include, but is not limited to: a hierarchical database, a network database, a relational database, an object database, an object-oriented database, an object-relational database, a spatial database two or more databases, and the like. The database 502 stores data in memory, which may be, but not limited to: read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, CDROM, digital versatile disk (DVD), or other optical or holographic storage media; magnetic cassettes, magnetic tape, magnetic disk storage device or other magnetic memory devices, wave carriers or other storage medium that can be used to store the required information, and which can be accessed by means of the device 501 converting a structured source data array and server 503. The database 502 is used to store data representing at least computer instructions or codes stored in memory, designed to perform pre Structured the transformations of the original data array transformation structured original data array components forming bonds card converted structured initial array data search the relevant information in converted structured source data arrays; to be converted or transformed one or more structured source data arrays containing at least natural language text that can be loaded into memory 5013 of device 501; and other data necessary for the functioning of the system. An exemplary system 500 further comprises a server computing device (server) 503 that stores and facilitates manipulation of computer instructions or codes previously described herein, which, accordingly, are not further described. Server 503 may be a personal computer, a laptop computer, a tablet computer, a handheld computer, a smartphone, a database machine, and the like. Server 503 provides data exchange control in the structured data source conversion system 500 and also provides data processing provided that one or more of the structured data array conversion devices 501 is connected to it or when the structured data array conversion device 501 is a thin client. In this case, all the computing power necessary to ensure the conversion of the structured data array is located on the server 503. The system 500 also contains one or more data networks 504. Data network 504 may include, but is not limited to, one or more local area networks (LANs) and / or wide area networks (WANs), or may be an information and telecommunications network Internet, or an Intranet, or a virtual private network (VPN) , or a combination thereof, and the like. The server 503 also has the ability to provide a virtual computing environment (Virtual Machine) to facilitate interaction between the device 501 converting the structured data array and the database 502. The network 504 is used to provide interaction between the device 501, the database 502, and the server 503 of the system 500.

Claims (18)

1. A method of converting a structured source data array containing at least natural language text, said method comprising at least the steps of:
A) form the original data structure of a structured source data array containing the source elements of the original data structure, and the said source elements are:
structurally complex linguistic constructions, which are structurally complex linguistic constructions containing contextual terms, and structurally complex linguistic constructions that do not contain contextual terms; and
structurally simple language constructs, which are structurally simple language constructs containing contextual terms, and structurally simple language constructs that do not contain contextual terms;
B) identify context terms in said source elements and form a database of context terms containing said identified context terms;
B) using the information contained in said generated contextual terms database, structurally complex linguistic constructs containing contextual terms, structurally complex linguistic constructs not containing contextual terms, structurally simple linguistic constructs containing contextual terms are identified in said initial data structure , and structurally simple language constructs that do not contain contextual terms;
D) carry out the conversion of the first type on the above-mentioned structurally complex language constructs that do not contain contextual terms to obtain grammatically and spelling-correct structurally simple language constructs that do not contain contextual terms, and the conversion of the first type includes format conversion and linguistic conversion;
E) carry out the conversion of the second type over the mentioned structurally simple language constructs containing contextual terms, to obtain grammatically and spelling correct structurally simple language constructs that do not contain contextual terms, and the conversion of the second type includes contextual conversion and linguistic conversion;
E) carry out the conversion of the third type over the above-mentioned structurally complex linguistic constructs containing contextual terms to obtain grammatically and spelling-correct structurally simple linguistic constructs that do not contain contextual terms, and the conversion of the third type includes formatting, contextual conversion and linguistic conversion;
G) form the final data structure of the structured initial data array containing the elements of the final data structure, and the elements of the final data structure are structurally simple language constructs that do not contain contextual terms and are grammatically and spelling correct due to transformations of the first, second and third types structurally simple language constructs that do not contain contextual terms. 2. The method according to p. 1, characterized in that it further comprises at least the stages in which:
C) on the basis of the resulting final data structure form the first data structure of the structured source data array containing the elements of the said first data structure, said elements of the first data structure containing the first logical partitions and second logical partitions;
I) form a database of logical connections of logical sections of the mentioned elements of the first data structure;
K) form a second data structure of a structured source data array containing the elements of said second data structure, said elements of the second data structure containing logical structures of logical partitions of said elements of the first data structure formed using information from said logical linking database of logical partitions, said logical sections contain the first semantic parts and the second semantic parts;
K) form a database of semantic parts of logical sections from said second semantic parts, wherein said second semantic parts are excluded from the corresponding mentioned logical sections;
M) form grammatically and orthographically correct semantic parts of the mentioned logical sections by linguistic transformations over the mentioned semantic parts;
H) form the resulting data structure of the structured source data array containing the elements of the said resulting data structure, said elements of the resulting data structure containing logical constructs containing at least the grammatically and spelling-correct semantic parts of logical sections. 3. The method according to p. 2, characterized in that stage H) is characterized by at least the stages in which:
- identify the final data structure of the structured source data array;
- identify the elements of the final data structure;
- identify the first logical sections of the said elements of the final data structure and the second logical sections of the said elements of the final data structure; and
- form the first data structure of the structured source data array containing the elements of the aforementioned first data structure, said elements of the first data structure containing the first logical partitions and second logical partitions. 4. The method according to p. 2, characterized in that stage I) is characterized by at least the stages in which:
- identify the elements of the first data structure containing one mentioned first logical partition, and the elements of the first data structure containing one mentioned second logical partition;
- identify elements of the first data structure containing more than one of the first logical partition, and elements of the first data structure containing more than one of the second logical partition;
- among the elements of the first data structure containing more than one of said first logical partitions, and in the elements of the first data structure containing more than one of said second logical partitions, logical relationships between said first logical partitions or between said second logical partitions are identified;
- among the elements of the first data structure containing more than one of the first logical partitions, and in the elements of the first data structure containing more than one of the second logical partitions, the elements of the first data structure that do not have a logical connection between logical partitions are identified; and
- form a database of logical relationships of logical sections of the elements of the first data structure. 5. The method according to p. 2, characterized in that stage K) is characterized by at least the stages in which:
- form logical structures of logical partitions of elements of the first data structure using information from a database of logical connections of logical partitions of elements of the first data structure and logical partitions of said elements of the first data structure containing one of the first logical partitions and logical partitions of said elements of the first data structure containing one said second logical partition; and
- form a second data structure of the structured source data array containing the elements of the second data structure, said elements of the second data structure being formed logical structures of logical partitions of the first data structure. 6. The method according to p. 2, characterized in that stage A) is characterized by at least the stages in which:
- identify the first logical partitions of the elements of the second data structure and the second logical partitions of the elements of the second data structure;
- in the aforementioned first logical sections and second logical sections of the elements of the second data structure, the first semantic parts and the second semantic parts are identified; and
- in said first and second logical sections of the elements of the second data structure, at least special semantic parts of the first logical sections of the elements of the second data structure and the special semantic parts of the second logical sections of the elements of the second data structure are identified and form a database of special semantic parts of the logical sections of the elements of the second data structures by moving said special semantic parts to said logically generated database of special semantic parts Partition elements of the second data structure. 7. The method according to p. 2, characterized in that stage M) is characterized by at least the stages in which:
- in said second semantic parts, said second logical sections of the elements of the second data structure, at least clarifying structures of the second semantic parts of the second logical sections are identified; and
- carry out linguistic transformations over all semantic parts, with the exception of the mentioned special semantic parts of the mentioned first and second logical sections, to form grammatically and spelling-correct semantic parts of logical sections of the elements of the second data structure. 8. The method according to p. 2, characterized in that stage H) is characterized by at least the stages in which:
- form semantic combinations of grammatically and spelling correct semantic parts of the second logical sections of the elements of the second data structure from the first grammatically and spelling correct semantic parts of the second logical sections of the elements of the second data structure ; and
- form the resulting data structure of the structured source data array containing the elements of the resulting data structure, and said elements of the resulting data structure are logical constructs containing the grammatically and spelling correct semantic parts of the logical sections of the elements of the second data structure. 9. The method of claim 8, wherein said logical constructs from said resultant data structure may further comprise said semantic combinations of grammatically and spelling-correct semantic parts of second logical sections of elements of the second data structure. 10. The method according to p. 1, characterized in that it further comprises at least the stages in which:
H) identify the final data structure of the original structured data array; identify elements of the resulting data structure; identifying the first logical sections of said elements of the resulting data structure and the second logical sections of said elements of the resulting data structure; and form the first data structure of the original structured data array containing the elements of said first data structure, said elements of the first data structure containing first logical partitions and second logical partitions;
I) identify elements of the first data structure containing one mentioned first logical partition, and elements of the first data structure containing one mentioned second logical partition; identifying elements of a first data structure containing more than one of said first logical partitions, and elements of a first data structure containing more than one of said second logical partition; in the elements of the first data structure containing more than one of said first logical partitions, and in the elements of the first data structure containing more than one of said second logical partitions, logical relationships between said first logical partitions or between said second logical partitions are identified; in the elements of the first data structure containing more than one of said first logical partitions, and in the elements of the first data structure containing more than one of said second logical partitions, elements of the first data structure that do not have a logical connection between logical partitions are identified; and form a database of logical relationships of logical partitions of the elements of the first data structure;
K) form logical structures of logical partitions of elements of the first data structure using information from a database of logical connections of logical partitions of elements of the first data structure and logical partitions of said elements of the first data structure containing one of the first logical partitions and logical partitions of said elements of the first data structure, containing one said second logical partition; and form a second data structure of the original structured data array containing elements of the second data structure, said elements of the second data structure being formed logical structures of logical partitions of the first data structure;
K) identify the first logical partitions of the elements of the second data structure and the second logical partitions of the elements of the second data structure; in said first logical sections and second logical sections of the elements of the second data structure, the first semantic parts and the second semantic parts are identified; and in said first and second logical partitions of the elements of the second data structure, at least special semantic parts of the first logical partitions of the elements of the second data structure and the special semantic parts of the second logical partitions of the elements of the second data structure are identified and form a database of special semantic parts of the logical partitions of the elements of the second data structures by moving said special semantic parts to said logically generated database of special semantic parts x sections of the elements of the second data structure;
M) in said second semantic parts of said second logical sections of the elements of the second data structure, at least clarifying structures of the second semantic parts of the second logical sections are identified and linguistic transformations are performed on all semantic parts, except for the mentioned special semantic parts of the said first and second logical sections , for the formation of grammatically and spelling-correct semantic parts of the logical sections of the elements of the second structure, yes data;
H) form semantic combinations of grammatically and spelling correct semantic parts of the second logical sections of the elements of the second structure from the first grammatically and spelling correct semantic parts of the second logical sections of the elements of the second data structure data; and form the resulting data structure of the original structured data array containing the elements of the resulting data structure, said elements of the resulting data structure being logical constructs containing the grammatically and spelling-correct semantic parts of the logical sections of the elements of the second data structure. 11. The method according to p. 10, characterized in that said logical constructions from said resultant data structure may further comprise said semantic combinations of grammatically and spelling-correct semantic parts of second logical sections of elements of the second data structure. 12. A device for converting a structured source data array containing at least:
one or more processors;
one or more input / output (I / O) modules; and
a memory containing program code, which upon execution causes said one or more processors of said device and / or device associated with said device to perform method steps according to any one of claims. 1-11, and containing one or more structured source data arrays to be converted containing at least natural language text. 13. The device according to p. 12, characterized in that said one or more structured source data arrays to be converted are downloadable, and said device is configured to connect to a database in which said loadable one or more structured source data arrays to be converted are stored , for loading into the aforementioned device memory of at least one loaded structured source array to be converted to data. 14. A system for converting a structured source data array, comprising at least:
one or more devices made in the form of devices according to any one of paragraphs. 12 or 13;
one or more servers providing regulation of data exchange in said system;
one or more databases for storing data configured to interact with said one or more devices;
one or more data transmission networks through which said devices, servers and databases interact. 15. The system according to p. 14, characterized in that the method according to any one of paragraphs. 1-11 is carried out by one or more of said servers, and said devices are a thin client. 16. The system of claim 15, wherein said database is used to store data representing at least one of: program code, which upon execution prompts said one or more processors of said device and / or device connected with the said device, perform the steps of the method according to any one of paragraphs. 1-11, one or more structured source data arrays to be converted containing at least natural language text. 17. The system according to any one of paragraphs. 14-16, characterized in that the data network is one of: local area network (LAN), wide area network (WAN), information and telecommunication network Internet, virtual private network (VPN). 18. A computer-readable storage medium containing program code that, when executed, causes the processor or processors of the device with which the computer-readable storage medium interacts to perform the steps of the method according to any one of claims. 1-11.

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