RU2544739C1 - Method to transform structured data array - Google Patents

Method to transform structured data array Download PDF

Info

Publication number
RU2544739C1
RU2544739C1 RU2014111223/08A RU2014111223A RU2544739C1 RU 2544739 C1 RU2544739 C1 RU 2544739C1 RU 2014111223/08 A RU2014111223/08 A RU 2014111223/08A RU 2014111223 A RU2014111223 A RU 2014111223A RU 2544739 C1 RU2544739 C1 RU 2544739C1
Authority
RU
Russia
Prior art keywords
data structure
elements
logical
containing
semantic parts
Prior art date
Application number
RU2014111223/08A
Other languages
Russian (ru)
Inventor
Игорь Петрович Рогачев
Original Assignee
Игорь Петрович Рогачев
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Игорь Петрович Рогачев filed Critical Игорь Петрович Рогачев
Priority to RU2014111223/08A priority Critical patent/RU2544739C1/en
Application granted granted Critical
Publication of RU2544739C1 publication Critical patent/RU2544739C1/en

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20Handling natural language data
    • G06F17/27Automatic analysis, e.g. parsing
    • G06F17/273Orthographic correction, e.g. spelling checkers, vowelisation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20Handling natural language data
    • G06F17/27Automatic analysis, e.g. parsing
    • G06F17/274Grammatical analysis; Style critique

Abstract

FIELD: information technologies.
SUBSTANCE: in the method of structured data array transformation, which contains text in natural language, they create (101) the first data structure of the structured data array from the end data structure of the structured data array. They create (102) a data base of logical connections between logical sections of elements of the first data structure. They create (103) the second data structure of the structured data array. They create (104) a data base of semantic parts of logical sections of elements of the second data structure. They create (105) grammatically and orthographically correct semantic parts of logical sections of the second data structure elements by means of linguistic transformations over the specified semantic parts. They create (106) the end data structure of the structured data array.
EFFECT: creation of logically, grammatically and orthographically true data structure, providing for quick and convenient navigation by structure elements.
17 cl, 15 dwg, 3 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 transformation 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 by they determine the set of unique blocks of information included in the selected fragment of the document and expand it by pre-processing 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 the operation is used as preliminary processing receiving at least one unique block of information, one or more blocks of information associated with a unique block of information and the predetermined ratio.

From the 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 extracting elements from the input data that correspond to patterns that are included in taxa that form 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 the fact that when compiling sets of vectors, only those elements that correspond to patterns included in one taxon or in taxons 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 conversion of data objects of source electronic documents by marking data objects of source electronic documents with hyperlinks, visualization of electronic documents with hyperlinks at the subscriber terminal 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 a link 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 GM BERIEV”), a method of forming 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 is determined 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 content of a document by highlighting unified constant information and variable information, storing constant information databases, entering constant information in a unified form of the document and introducing variable information into the document, in which the variable un is allocated in the information variable 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 They form the lexical tree of a document by defining interdependency separate support and then form the word document information control loop by setting method variable unified language implementation and non-unified information depending on the nature of the bond with the fragment of the reference words of the document.

From WO 2013043160 A1, G06F 17/21, 03/28/2013 (HEWLETT PACKARD DEVELOPMENT WITH 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 peculiarity of the selected fragment from the text, and the said nodes of the graph are interconnected by relations similar to the relationship 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, 04/04/2001 (TNV 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 (DOS), as the name of the folders that contain parts of the LMS and the destination with the specified folders 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 patent US 8229730 B2, G06F 17/30, 24.07.2012 (MICROSOFT CORP ET AL.) There is a method of searching for data at the request of a user presented in natural language, the method consisting in parsing a text data array 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 (TNBENTA 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 with 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 transform zovyvayutsya 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, September 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 a template of semantic links, with semantic labels associated with words or phrases of sentences of the text, 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 be solved by the claimed invention is aimed at providing such processing of a structured data array containing natural language text that would allow generating a logically, grammatically and spelling-correct transformed structure containing the logical structures of the array elements and providing quick and convenient navigation by array elements.

The technical result is the formation of a logically, grammatically and spelling-correct data structure suitable for quick and convenient navigation through structural elements.

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

A) form a first data structure of a 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;

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

B) form a second data structure of a structured 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 generated using information from said logical linking database of logical partitions, said logical sections contain the first semantic parts and the second semantic parts;

D) 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;

E) form the grammatically and spelling-correct semantic parts of the mentioned logical sections by linguistic transformations over the mentioned semantic parts;

E) form the final data structure of the structured data array containing the elements of the said final data structure, said elements of the final data structure containing logical structures containing at least the grammatically and spelling-correct semantic parts of logical sections.

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:

Figure 1 shows a General diagram of the steps of the claimed method of converting a structured data array containing at least natural language text.

Figure 2 shows a General diagram of the stage of formation of the first data structure.

Figure 3 shows the general structure of the original data structure from which the first data structure is formed.

Figure 4 shows a General diagram of the stage of forming a database of logical connections of logical partitions.

Figure 5 shows the general principle of forming a database of logical connections of logical partitions.

Figure 6 shows a General diagram of the stage of formation of the second data structure.

7 shows the general structure of a second data structure.

On Fig shows a General diagram of the stage of forming a database of semantic parts.

Figure 9 shows the general principle of forming a database of semantic parts.

Figure 10 shows a General diagram of the stage of formation of grammatically and spelling correct semantic parts.

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

On Fig shows a General diagram of the stage of formation of the final data structure.

On Fig shows a General diagram of the final data structure.

On Fig shows the General structure of the elements of the final data structure.

On Fig shows a General diagram of a system for converting a structured data array containing a device for converting a structured 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 text in a natural language, which is, but is not limited to, legal acts (NLA). It should be obvious to a person skilled in the art that, although 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 the 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 on the basis of the presumption of knowledge of the law and impose sanctions for its non-fulfillment.

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, encapsulated in the structure of the regulatory prescription, which, in turn, is an element (part) of the rule of law (legal norm).

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

A) form the first data structure of a structured 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;

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

C) form 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 logical linking database of logical partitions, said logical sections contain the first semantic parts and the second semantic parts;

D) 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;

E) form the grammatically and spelling-correct semantic parts of the mentioned logical sections by linguistic transformations over the mentioned semantic parts;

E) form the final data structure of the structured data array containing the elements of the said final data structure, said elements of the final data structure containing logical structures containing at least the grammatically and spelling-correct semantic parts of logical sections.

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

A) identify the original data structure of a 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 a first data structure of a 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;

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

C) form 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 logical linking database of logical partitions, said logical sections contain the first semantic parts and the second semantic parts;

D) 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;

E) form the grammatically and spelling-correct semantic parts of the mentioned logical sections by linguistic transformations over the mentioned semantic parts;

E) form the final data structure of the structured data array containing the elements of the said final data structure, said elements of the final data structure containing logical structures containing at least the grammatically and spelling-correct semantic parts of logical sections.

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

A) form a first data structure of a 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;

B) identify the elements of the first data structure containing one mentioned first logical section, and the elements of the first data structure containing one mentioned second logical section; 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;

B) form a second data structure of a structured 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 generated using information from said logical linking database of logical partitions, said logical sections contain the first semantic parts and the second semantic parts;

D) 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;

E) form the grammatically and spelling-correct semantic parts of the mentioned logical sections by linguistic transformations over the mentioned semantic parts;

E) form the final data structure of the structured data array containing the elements of the said final data structure, said elements of the final data structure containing logical structures 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 for converting a structured data array containing at least natural language text, said method comprising at least the steps of:

A) form the first data structure of a structured 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;

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

C) 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 the said elements of the first data structure, containing one said second logical partition; and form a second data structure 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;

D) 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;

E) form the grammatically and spelling-correct semantic parts of the mentioned logical sections by linguistic transformations over the mentioned semantic parts;

E) form the final data structure of the structured data array containing the elements of the said final data structure, said elements of the final data structure containing logical structures containing at least the grammatically and spelling-correct semantic parts of logical sections.

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

A) form a first data structure of a 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;

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

B) form a second data structure of a structured 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 generated using information from said logical linking database of logical partitions, said logical sections contain the first semantic parts and the second semantic parts;

D) 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;

E) form the grammatically and spelling-correct semantic parts of the mentioned logical sections by linguistic transformations over the mentioned semantic parts;

E) form the final data structure of the structured data array containing the elements of the said final data structure, said elements of the final data structure containing logical structures containing at least the grammatically and spelling-correct semantic parts of logical sections.

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

A) form a first data structure of a 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;

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

B) form a second data structure of a structured 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 generated using information from said logical linking database of logical partitions, said logical sections contain the first semantic parts and the second semantic parts;

D) 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;

E) 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;

E) form the final data structure of the structured data array containing the elements of the said final data structure, said elements of the final data structure containing logical structures containing at least the grammatically and spelling-correct semantic parts of logical sections.

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

A) form a first data structure of a 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;

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

B) form a second data structure of a structured 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 generated using information from said logical linking database of logical partitions, said logical sections contain the first semantic parts and the second semantic parts;

D) 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;

E) form the grammatically and spelling-correct semantic parts of the mentioned logical sections by linguistic transformations over the mentioned semantic parts;

E) 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 orthographically correct semantic parts of the second logical sections of the elements of the second data structure and the grammatically and spelling correct semantic parts of the second semantic parts of the second logical sections of the elements of the second data structure data; and form the final data structure containing the elements of the final data structure, and the said elements of the final data structure are logical constructs containing the mentioned grammatically and spelling correct semantic parts of the logical sections of the elements of the second data structure.

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

A) form a first data structure of a 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;

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

B) form a second data structure of a structured 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 generated using information from said logical linking database of logical partitions, said logical sections contain the first semantic parts and the second semantic parts;

D) 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;

E) form the grammatically and spelling-correct semantic parts of the mentioned logical sections by linguistic transformations over the mentioned semantic parts;

E) 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 orthographically correct semantic parts of the second logical sections of the elements of the second data structure and the grammatically and spelling correct semantic parts of the second semantic parts of the second logical sections of the elements of the second data structure data; and form the final data structure containing the elements of the final data structure, and the said elements of the final data structure are logical constructs containing the mentioned grammatically and spelling correct semantic parts of the logical sections of the elements of the second data structure; moreover, said logical constructions from said final data structure may additionally contain said formed semantic combinations of grammatically and spelling-correct semantic parts of second logical sections of elements of the second data structure.

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

A) identify the original data structure of a 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 a first data structure of a 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;

B) identify the elements of the first data structure containing one mentioned first logical section, and the elements of the first data structure containing one mentioned second logical section; 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;

C) form logical structures 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 partition; and form a second data structure 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;

D) 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;

E) 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;

E) 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 orthographically correct semantic parts of the second logical sections of the elements of the second data structure and the grammatically and spelling correct semantic parts of the second semantic parts of the second logical sections of the elements of the second data structure data; and form the final data structure containing the elements of the final data structure, and the said elements of the final data structure are logical constructs containing the mentioned 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 for converting a structured data array containing at least natural language text, said method comprising at least the steps of:

A) identify the original data structure of a 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 a first data structure of a 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;

B) identify the elements of the first data structure containing one mentioned first logical section, and the elements of the first data structure containing one mentioned second logical section; 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;

C) 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 the said elements of the first data structure, containing one said second logical partition; and form a second data structure 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;

D) 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;

E) 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;

E) 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 orthographically correct semantic parts of the second logical sections of the elements of the second data structure and the grammatically and spelling correct semantic parts of the second semantic parts of the second logical sections of the elements of the second data structure data; and form the final data structure containing the elements of the final data structure, and the said elements of the final data structure are logical constructs containing the mentioned grammatically and spelling correct semantic parts of the logical sections of the elements of the second data structure; moreover, said logical constructions from said final data structure may additionally contain said formed semantic combinations of grammatically and spelling-correct semantic parts of second logical sections of elements of the second data structure.

Moreover, for a specialist in the field of technology to which the present invention relates, it should be obvious that the second to tenth embodiments characterize 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 an eleventh embodiment of the present invention, there is provided a device for converting a 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 of the first to tenth embodiments of the present invention and containing one or more structured ones to be converted data sets containing at least natural language text.

In a twelfth embodiment of the present invention, there is provided a device for converting a 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 of the first to tenth embodiments of the present invention and containing one or more structured ones to be converted data arrays containing at least natural language text, said one or more structured data arrays to be converted s are downloadable, and said device is configured to connect to a database in which said downloadable one or more structured data arrays to be converted are stored to load at least one loadable structured data array to be converted into said device memory.

In a thirteenth embodiment of the present invention, there is provided a structured data array transformation system comprising at least:

one or more devices made in the form of devices according to any one of the eleventh or twelfth 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.

In a fourteenth embodiment of the present invention, there is provided a structured data array transformation system comprising at least:

one or more devices made in the form of devices according to any one of the eleventh or twelfth 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 tenth embodiments of the present invention is carried out by one or more of said servers, and said devices are a thin client.

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

one or more devices made in the form of devices according to any one of the eleventh or twelfth 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 tenth 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 tenth of the present invention, one or more structured data arrays to be converted containing at least natural language text to be converted.

In a sixteenth embodiment of the present invention, there is provided a system according to any of the thirteenth to fifteenth 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 seventeenth 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 the first to tenth embodiments of the present invention.

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.

Figure 1 as an example, but not limitation, shows a General diagram of the steps of the claimed method 100 for converting a structured data array containing at least natural language text. The claimed method 100 for converting a structured data array containing at least natural language text is characterized by performing step 101 of generating a first data structure in which a first data structure of a structured data array containing elements of said first data structure is formed, said elements of the first structure data contains first logical partitions and second logical partitions; performing step 102 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 103 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 104 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 105 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 106 of forming the final data structure, which forms the final data structure of the structured data array containing the elements of the said final data structure, said elements of the final data structure containing logical constructs containing at least the grammatically and spelling-correct semantic parts of the logical sections.

Figure 2 as an example, but not limitation, shows a General diagram of the steps of step 101 of the formation of the first data structure. Step 101 is characterized by performing step 1011 of identifying the source structure, in which the source data structure 1 of the structured data array is identified; performing step 1012 of the identification of the elements, which identify the elements 11 of the original data structure 1; by performing logical partition identification step 1013, in which the first logical partitions 111 of the elements 11 of the original data structure 1 and the second logical partitions 112 of the elements 11 of the original data structure 1 are identified; and performing step 1013 of forming the first data structure, which forms the first data structure 2 of the structured data array containing the elements 21 of the first data structure, which are the elements 11 of the original data structure 1, the elements 21 of the first data structure 2 containing the first logical sections 111 of the elements 11 the original data structure 1, and containing the elements 22 of the first data structure, the elements 22 of the first data structure 2 containing the second logical sections 112, elements 11 of the original data structure 1.

Figure 3 shows, by way of example, but not limitation, the general structure of the original data structure 1 from which the first data structure 2 is formed. The original data structure 1 is a structured 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 original data structure 1 contains elements 11, which are positions that are sentences - grammatically organized word combinations. Moreover, each sentence is characterized by semantic completeness. The identification of sentences at step 1012 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 step 1012, the identification of elements reveals elementary semantic units - sentences that are judgments. 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 the elements 11 of the initial data structure 1 is carried out by identifying and defragmenting the sentence into the primary components of the sentence, namely 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 the elements 11 of the original data structure 1 is carried out, whereby the elements of simple judgments are identified in the 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 judgment “Every crime is an illegal act”, the subject of the judgment is the concept of “crime”, the predicate of the judgment is “illegal act”, the connective is expressed by a dash, and the quantifier word “every” indicates that the characteristic “illegal act” refers to 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 initial 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 original 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.

Figure 00000001

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.

Figure 00000002

In order to identify the first logical sections 111 and the second logical sections 112 of the elements 11 of the mentioned initial 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 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 is an indicator of the 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 101 contains elements such as sentences (element 11 of the original structure data 1) and the judgment logical partitions 111 or 112 members 11 original data structure 1. In this case, judgment is further identified by said logical link as a base, i.e., as the first logical section 111 of the element of the original 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 having the first logical partitions 111 are formed, and elements 22 of the first data structure 2 having second logical partitions 112.

Figure 4 as an example, but not limitation, shows a General diagram of the steps of step 102 of forming a database 3 of logical connections of logical partitions. The step 102 of creating a database 3 of logical connections of logical partitions is characterized by the execution of step 1021 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 partition 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 1022 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 1023 of identifying the 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 1024 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 structure are identified 2 data that do not have logical connections between their logical partitions; and the implementation of step 1025 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, it is necessary to separate 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. Moreover, 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 identified elements 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 as separate elements of a given array of elements. In turn, each element in the arrays of elements 32, 34 containing the second logical partition 112 is identified as element 32 having only one logical partition 112, or as element 34 having more than one logical partition 112. In this case, if any in the 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 still associated with the element from which they are selected and for this reason identified ovany as separate elements of the array 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 conjunction elements; " " - 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 "initial" propositions by means of a 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: “AVB”. 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, elements of the arrays of elements should 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 judgment) remains unidentified from the point of view of the logical nature of the judgment, 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 judgment 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. 5).

6, by way of example, but not limitation, the general flowchart of the steps of step 103 of generating the second data structure 4 is shown. Step 103 of the formation of the second data structure 4 is characterized by the execution of step 1031 of generating logical structures, in which logical structures 41 of logical sections 111 are formed, 112 elements 31, 32, 33, 34 of the first data structure 2, using information from the database 3 of logical connections of logical partitions of elements 31, 32, 33, 34 of the first data structure 2 and logical partitions of the mentioned elements 31 per 2 nd data structure comprising one said first logical partition 111 and logical partition of said data elements 32 of the first structure 2, comprising one said second logical partition 112; and performing step 1032 of generating a second data structure 4, wherein a second data structure 4 is formed comprising elements 41 of a second data structure 4, said elements of the second data structure 4 being formed logical structures 41 of logical partitions 111, 112 of elements 31, 32, 33 , 34 of the first data structure 2. Logical constructions 42 are the result of transforming the data of the transformed 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 regulation 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” (see table 3). 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. regulatory requirements, it is also necessary to take into account 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 42 are reduced to admissible combinations of logical connections between the same type of judgments within the framework of a single normative prescription.

Figure 00000003

Moreover, if a sentence contains several situations united by the logic “OR” (weak disjunctive connection), this means that each of these situations forms its own separate instructions with the same rules that were used in the first order. 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 regulations with the same situations that were used in the first regulation. This means that several “rules” with such “OR” logic cannot be in one prescription. The second data structure formed in the above manner contains elements such 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. 7). 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).

On Fig as an example, but not limitation, shows a General diagram of the stages of step 104 of the formation of the database 5 semantic parts. The step 104 of the formation of the database of 5 semantic parts is characterized by the execution of step 1041 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; performing step 1042 of identifying the semantic parts, in which in the first logical sections 411 and second logical sections 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 1043 of identifying the specific semantic parts, in which at least the specific semantic parts 4111 of the first logical sections 411 of the elements 41 of the second data structure 4 and the 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 the said generated database 5 of special semantic parts of the logical sections of the elements 41 of the second data structure 4 (Fig. 9). 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 4110 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. 9). 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) .

Figure 10 shows, by way of example, but not limitation, the general flowchart of the steps of step 105 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 105 includes performing a refinement structure identification step 1051, 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 step 1052 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 mentioned first and second logical sections 411, 412, to form grammatically and spelling-correct semantic parts 4123 and refinement structures 4122 of logical sections of the elements 41 of the second data structure 4. The General scheme of the obtained second data structure 4 is presented in Fig.11. 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 the 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.

12, by way of example, but not limitation, a general flowchart of the steps of step 106 of generating the final data structure 6 is shown, in which the final data structure 6 of the structured data array containing the elements 61 of the said final data structure 6 is formed, said elements 61 of the final data structures contain logical constructs 61 containing at least the grammatically and spelling-correct semantic parts 4123 of logical partitions mentioned. Step 106 is characterized by the execution of the semantic combination step 1061, in which the second grammatical and spelling correct semantic parts 4123 of the second logical sections 412 of the elements 41 of the second data structure 4 and the grammatically and spelling correct refinement structures 4122 of the second semantic parts 4120 of the second logical sections of the 412 elements are formed 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 performing step 1062 of forming the final data structure 6, which forms the final data structure 6 containing the elements 61 of the final data structure 6, said elements 61 of the final 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. It is also possible that the logical constructs 61 of said final data structure 6 further comprise the old 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. 13). The final overall structure of element 61 of the final data structure 6 is shown in FIG. 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 identified and the rest of the conditions were also highlighted in the second semantic parts 4120, in which some of these parts are identified as refinement 4124. As a result of all the transformations, it became possible in the logical structure of their constructions 61 to create semantic combinations 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 final data structure is a structured structure, the elements of which correspond to the structure of the regulatory prescription as much as possible. The final data structure is formed in this form in order to simplify and regulate professional work on the creation and adjustment of legal acts. The final 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.

15, by way of example, but not limitation, an exemplary diagram of the inventive structured data array conversion system 200 is illustrated, which in a preferred embodiment includes at least one or more structured data array conversion devices 201 comprising at least one or more processors 2011, one or more input / output (I / O) modules 2012, and memory 2013. Said structured data array conversion devices 201 may be, but not limited to : personal computer, laptop computer, tablet computer, PDA, smartphone, thin client and the like. The memory (computer-readable storage medium) 2013 of the structured data array conversion device 201 contains a program code which, when executed, causes said one or more processors 2011 of said device 201 and / or device 201 associated with said device 201 to perform the steps of the structured data conversion method described above data array, and contains one or more structured data arrays to be converted, containing at least natural language text. Moreover, one or more structured data arrays to be converted may be downloadable and stored, in particular, in the database 203 of the structured data array transformation 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, a procedure for converting a structured data array can be carried out. The device contains one or more processors 2011, which are designed to execute computer instructions or codes stored in the device’s memory in order to ensure the implementation of the procedure for converting a structured data array. Computer instructions or codes stored in memory are designed to perform the conversion of a structured data array. These commands and codes include at least commands for generating the first data structure of a structured data array, commands for generating a database of logical connections, commands for generating a second data structure for a structured data array, commands for generating a database of semantic parts of logical sections, and commands for generating grammatically and spelling correct semantic parts, commands for the formation of the final data structure of a structured data array and the corresponding commands, intended nnye to execute the commands mentioned above. The I / O 2012 modules of device 201 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 2012 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. System 200 may also include a database (DB) 202. DB 202 can be, 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, a combination listed two or more databases, and the like. The DB 202 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 data carriers; 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 the structured array data conversion device 201 and server 203. The database 202 is used to store data representing at least a command for generating a first data structure of a structured data array, a command for generating logical link databases, commands for generating a second data structure for a structured data array, commands for generating a database of semantic parts of logical partitions, commands for generating grammatically and spelling correct semantic parts, commands for generating a final data structure for a structured data array, and corresponding commands for executing said commands ; one or more structured data arrays to be converted containing at least natural language text that can be loaded into the memory 2013 of the structured data array transform device 201; and other data necessary for the functioning of the system. An exemplary structured data array conversion system 200 further comprises a server computing device (server) 203 that stores and facilitates manipulation of computer instructions or codes previously described herein, which, accordingly, are not further described. Server 203 may be a personal computer, a laptop computer, a tablet computer, a handheld computer, a smartphone, a database machine, and the like. The server 203 provides data exchange control in the structured data array conversion system 200, and also provides data processing provided that one or more of the structured data array conversion devices 201 is connected to it or when the structured data array conversion device 201 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 203. The system 200 also includes one or more data networks 204. Data network 204 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 203 also has the ability to provide a virtual computing environment (Virtual Machine) to facilitate interaction between the structured data array conversion device 201 and the database 202. The network 204 serves to provide interaction between the device 201, the database 202 and the server 203 of the structured data array conversion system 200.

Claims (17)

1. A method of converting a structured data array containing at least natural language text, said method comprising at least the steps of:
A) form a first data structure of a 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;
B) form a database of logical connections of logical sections of the mentioned elements of the first data structure;
B) form a second data structure of a structured 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 generated using information from said logical linking database of logical partitions, said logical sections contain the first semantic parts and the second semantic parts;
D) 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;
E) form the grammatically and spelling-correct semantic parts of the mentioned logical sections by linguistic transformations over the mentioned semantic parts;
E) form the final data structure of the structured data array containing the elements of the said final data structure, said elements of the final data structure containing logical structures containing at least the grammatically and spelling-correct semantic parts of logical sections.
2. The method according to claim 1, characterized in that stage A) is characterized by at least the stages in which:
- identify the original data structure of the structured data array;
- identify the elements of the original data structure;
- identify 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 a 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.
3. The method according to claim 1, characterized in that stage B) 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.
4. The method according to claim 1, characterized in that stage B) 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 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.
5. The method according to claim 1, characterized in that step D) 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.
6. The method according to claim 1, characterized in that stage D) is characterized by at least the stages in which:
- 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.
7. The method according to claim 1, characterized in that step E) 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 final data structure containing the elements of the final data structure, and the said elements of the final 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.
8. The method according to claim 7, characterized in that said logical constructions from said final 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.
9. The method according to claim 1, characterized in that said method of converting a structured data array containing at least natural language text comprises at least the steps of:
A) identify the original data structure of a 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 a first data structure of a 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;
B) identify the elements of the first data structure containing one mentioned first logical section, and the elements of the first data structure containing one mentioned second logical section; 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;
C) 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 the said elements of the first data structure, containing one said second logical partition; and form a second data structure 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;
D) 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;
E) 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;
E) form semantic combinations of grammatically and spelling correct semantic parts of the second logical sections of 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 data; and form the final data structure containing the elements of the final data structure, and the said elements of the final data structure are logical constructs containing the mentioned grammatically and spelling correct semantic parts of the logical sections of the elements of the second data structure.
10. The method according to claim 9, characterized in that said logical constructions from said final 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.
11. A device for converting a structured 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 the actions of the method according to any one of claims 1 to 10, and containing one or more structured data arrays to be converted containing at least natural language text.
12. The device according to claim 11, characterized in that said one or more structured 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 data arrays to be converted are stored, for loading into said memory of the device of at least one loaded structured data array to be converted.
13. A system for converting a structured data array containing at least:
one or more devices made in the form of devices according to any one of claims 11 or 12 of the formula;
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.
14. The system according to item 13, wherein the method according to any one of claims 1 to 10 of the formula is carried out by one or more of the mentioned servers, and the said devices are a thin client.
15. The system of clause 14, wherein said database is used to store data representing at least one of: program code, which upon execution causes 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 claims 1 to 10 of the formula, to be converted one or more structured data arrays containing at least natural language text.
16. The system according to any one of paragraphs.13-15, characterized in that said data network is one of: local area network (LAN), wide area network (WAN), information and telecommunication network Internet, virtual private network (VPN).
17. A computer-readable storage medium containing program code, which upon execution causes the processor or processors of the device with which the computer-readable storage medium interacts, to perform the actions of the method according to any one of claims 1 to 10 of the formula.
RU2014111223/08A 2014-03-25 2014-03-25 Method to transform structured data array RU2544739C1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
RU2014111223/08A RU2544739C1 (en) 2014-03-25 2014-03-25 Method to transform structured data array

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
RU2014111223/08A RU2544739C1 (en) 2014-03-25 2014-03-25 Method to transform structured data array
PCT/RU2015/000322 WO2015147706A2 (en) 2014-03-25 2015-05-22 Method for converting a structured data array
EA201600675A EA033096B1 (en) 2014-03-25 2015-05-22 Method for converting a structured data array

Publications (1)

Publication Number Publication Date
RU2544739C1 true RU2544739C1 (en) 2015-03-20

Family

ID=53290749

Family Applications (1)

Application Number Title Priority Date Filing Date
RU2014111223/08A RU2544739C1 (en) 2014-03-25 2014-03-25 Method to transform structured data array

Country Status (3)

Country Link
EA (1) EA033096B1 (en)
RU (1) RU2544739C1 (en)
WO (1) WO2015147706A2 (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2670781C2 (en) * 2017-03-23 2018-10-25 Илья Николаевич Логинов System and method for data storage and processing
RU2685966C1 (en) * 2018-06-07 2019-04-23 Игорь Петрович Рогачев Method for lingual-logical transformation of a structured data array containing a linguistic sentence
RU2685960C1 (en) * 2018-06-07 2019-04-23 Игорь Петрович Рогачев Method of converting structured data array, containing syntactic units
RU2691836C1 (en) * 2018-06-07 2019-06-18 Игорь Петрович Рогачев Method of transforming a structured data array comprising main linguistic-logic entities

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2210809C2 (en) * 2000-11-21 2003-08-20 Открытое акционерное общество "Московская телекоммуникационная корпорация" Method for ordering data submitted in alphanumeric information blocks
RU2399959C2 (en) * 2008-10-29 2010-09-20 Закрытое акционерное общество "Авикомп Сервисез" Method for automatic text processing in natural language through semantic indexation, method for automatic processing collection of texts in natural language through semantic indexation and computer readable media
EP2400400A1 (en) * 2010-06-22 2011-12-28 Inbenta Professional Services, S.L. Semantic search engine using lexical functions and meaning-text criteria
RU2488877C2 (en) * 2007-08-31 2013-07-27 Майкрософт Корпорейшн Identification of semantic relations in indirect speech

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2012520527A (en) * 2009-03-13 2012-09-06 インベンション マシーン コーポレーション Question answering system and method based on semantic labeling of user questions and text documents

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2210809C2 (en) * 2000-11-21 2003-08-20 Открытое акционерное общество "Московская телекоммуникационная корпорация" Method for ordering data submitted in alphanumeric information blocks
RU2488877C2 (en) * 2007-08-31 2013-07-27 Майкрософт Корпорейшн Identification of semantic relations in indirect speech
RU2399959C2 (en) * 2008-10-29 2010-09-20 Закрытое акционерное общество "Авикомп Сервисез" Method for automatic text processing in natural language through semantic indexation, method for automatic processing collection of texts in natural language through semantic indexation and computer readable media
EP2400400A1 (en) * 2010-06-22 2011-12-28 Inbenta Professional Services, S.L. Semantic search engine using lexical functions and meaning-text criteria

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2670781C2 (en) * 2017-03-23 2018-10-25 Илья Николаевич Логинов System and method for data storage and processing
RU2670781C9 (en) * 2017-03-23 2018-11-23 Илья Николаевич Логинов System and method for data storage and processing
RU2685966C1 (en) * 2018-06-07 2019-04-23 Игорь Петрович Рогачев Method for lingual-logical transformation of a structured data array containing a linguistic sentence
RU2685960C1 (en) * 2018-06-07 2019-04-23 Игорь Петрович Рогачев Method of converting structured data array, containing syntactic units
RU2691836C1 (en) * 2018-06-07 2019-06-18 Игорь Петрович Рогачев Method of transforming a structured data array comprising main linguistic-logic entities

Also Published As

Publication number Publication date
WO2015147706A2 (en) 2015-10-01
EA033096B1 (en) 2019-08-30
EA201600675A1 (en) 2017-01-30
WO2015147706A9 (en) 2015-12-10
WO2015147706A3 (en) 2016-02-04

Similar Documents

Publication Publication Date Title
Lucas et al. Computer-assisted text analysis for comparative politics
Allamanis et al. Learning natural coding conventions
KR101004515B1 (en) Method and system for retrieving confirming sentences
US10037377B2 (en) Automated self-service user support based on ontology analysis
Al‐Sughaiyer et al. Arabic morphological analysis techniques: A comprehensive survey
US8065336B2 (en) Data semanticizer
US20110137919A1 (en) Apparatus and method for knowledge graph stabilization
US8442810B2 (en) Deep model statistics method for machine translation
US20110060584A1 (en) Error correction using fact repositories
KR20120009446A (en) System and method for automatic semantic labeling of natural language texts
US10127214B2 (en) Methods for generating natural language processing systems
US20120303661A1 (en) Systems and methods for information extraction using contextual pattern discovery
US7765097B1 (en) Automatic code generation via natural language processing
KR20080107383A (en) Adaptive semantic platform architecture
US9621601B2 (en) User collaboration for answer generation in question and answer system
JPH1078964A (en) Method and system for identifying and analyzing generally confused word by natural language parser
Carley et al. Automap user's guide 2012
CN101019113A (en) Computer-implemented method for use in a translation system
US9678949B2 (en) Vital text analytics system for the enhancement of requirements engineering documents and other documents
US20100121630A1 (en) Language processing systems and methods
DE112012005177T5 (en) Generating a natural language processing model for an information area
US9613317B2 (en) Justifying passage machine learning for question and answer systems
CN103778471B (en) Provide information gap indicated answering system
US9069750B2 (en) Method and system for semantic searching of natural language texts
US7299228B2 (en) Learning and using generalized string patterns for information extraction

Legal Events

Date Code Title Description
PC41 Official registration of the transfer of exclusive right

Effective date: 20171110