CN102087669B - Intelligent search engine system based on semantic association - Google Patents
Intelligent search engine system based on semantic association Download PDFInfo
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Abstract
The invention provides an intelligent search engine system based on semantic association realized by a predefined discipline ontology determining a discipline domain concept, comprising the following steps of: (1) creating the discipline ontology by a semantic association editor; (2) adding predefined associated word family to the discipline ontology; (3) determining the ontology related subjects and reference resource; (4) extracting related subjects of index articles by using the predefined ontology word family; (5) storing the extracted key items as attribute indexes; and (6) searching the contents by using the key items or key words, and clustering articles to the related ontology. The method can effectively define the discipline ontology, improve accuracy of searching the content in controlled environment (such as professional courseware resource in the school), and improve the related degree between the resources.
Description
Technical field
The present invention relates to the intelligent searching engine field, more particularly, relate to realization a kind of by body word family extraction feature word of text, and utilize the search engine system of theme dictionary assisted retrieval and guiding search process.
Background technology
Search engine is that text is carried out index, locates afterwards the computer system of required content in mass data by the inverted index that presets.Index is carried out obtaining behind the scanning analysis to text by Words partition system.Index has covered the relative words that content of text utilizes, and its data volume is huge and ambiguity is more, only has to mate fully when retrieval and just can find related content.This retrieval utilization to content of text has caused very large difficulty.In real world applications, the user wishes that more with the Query Result automatic cluster be own interested career field, obtains more useful informations by the guiding of classification again.Therefore, need to analyze content of text, by the feature key word of text, extract the theme of content.In Index process, because computing machine can't be understood the semantic relation of vocabulary, therefore, can only provide simple match search by glossarial index.
Body is the generalities about domain knowledge, formal clear and definite standard, is made of the concept in the field or vocabulary and the relation between them.The domain knowledge body in object content is searched for all similar match block according to different searching algorithms with matching principle as the organizer of one group of vocabulary in certain subject.At present, agreement word and search technology by the vocabulary correlativity is comparative maturity, agree that the word and search algorithm is that utilization is agreed that each word segmentation unit in the vocabulary hunting zone carries out matching operation to obtain the result of an optimum, vocabulary is up and down the complex relationships such as class and reference but this coupling is beyond expression.
For the content in the controlled environment, clearer and more definite identification vocabulary is often arranged between content type, so, stronger classification ownership is arranged between content.By the retrieval to unlabeled data, obtain the more multivariate data of content of text, for the mass data system provides more retrieval point, be to set up the architectonic important support key element of document.
Summary of the invention
Technology of the present invention is dealt with problems: overcome the deficiencies in the prior art, a kind of predefine subject body is provided, interrelated by body vocabulary, improving method and the software of search engine retrieving effect realizes, this system can effectively carry out domain knowledge to content of text and extract, improve the quality of full-text search, and the real concept retrieval capability.
In order to obtain above-mentioned technique effect, the technical solution adopted in the present invention is: realize the intelligent searching engine system of semantic-based association, its characteristics are to comprise:
Self-defined concept vocabulary module: a concept vocabulary editing machine, User Defined concept vocabulary is provided, sets up interface related between vocabulary, comprise: the definition of key concept word, the key concept word is one group of sign vocabulary in the relevant word family, behind definition key concept word, can be for related between its subordinate vocabulary of a concept definition, related notion and concept.It is input as the user and wishes the conceptual system relative words that define, is output as the concept system that uses OWL to describe, and comprising: key concept word, conceptual dependency vocabulary, conceptual dependency relation and related resource finger URL etc.
The concept vocabulary is derived module: it is the module of carrying out exchanges data between user concept definition and semantic search engine that the concept vocabulary is derived module, the concept vocabulary is derived module user-defined notional word tree is exported as the structured document that uses XML to describe, and uses so that search engine module to be provided.
The conceptual index module is the module of text being carried out the generalities index process, its input text of making a living, and the conceptual index module is carried out concept to text and is extracted, and the notional word after the extraction forms index file, uses in order in the future retrieval.Comprise following three submodules:
Search engine word-dividing mode: the text of input is divided into Chinese vocabulary, relates to the identification of name, place name, unit name and the judgement of ambiguity vocabulary.The basis of participle is the Chinese word segmentation vocabulary, and system will carry out cutting and vocabulary judgement to text by the definition in the participle vocabulary.
The Conceptual Projection module: text vocabulary and conceptual model are mated, and algorithm has: the simple words matching algorithm, based on the matching algorithm of keyword and based on word frequency with existing degree of correlation algorithm.System will be stored in the conceptional tree structure memory-resident in the database, and mate the notional word string of output matching success with word segmentation result.
The conceptual index generation module: according to the concept vocabulary generating indexes file that the Conceptual Projection module forms, indexed file structure is: reference number of a document, key concept word string, document location link, associated description content etc.
The conceptual retrieval module is the module that conceptual index is retrieved, and the user can navigate by conceptional tree, concept and range is shunk and the keyword retrieval function.
The conceptual retrieval module still can use the participle engine that the non-standard word of user's input is carried out word segmentation processing, and result will be sent to the Conceptual Projection module and mate.
The result for retrieval module is extracted the content with the concept terminology match in the conceptual index file, and extract conceptual dependency word and content and mate and carry out highlighted demonstration, the result is presented in the user interface, and displaying contents has: conceptual dependency word in document location link, text-dependent metadata, text concept relative words, the text (blue highlighted), original term (red highlighted).
The conceptual navigation module shows the conceptional tree hierarchical, and the deployable conceptional tree of user also selects certain node to inquire about, and is the method for system supplymentary user inquiry.
Result set convergence module is that user's Query Result is all shown in the concept that different dimensions relates to, thereby the concept of a certain dimension of user selection limits Query Result, is a kind of method of system optimization Query Result.
Principal right requires module to be in the above description: concept definition module, concept derive module, Conceptual Projection module, conceptual index generation and retrieval module.
Principle of the present invention is: the term in the Field Words has the stronger relation that separates, text can be pointed to particular topic when the relational language frequency of occurrence is more, so can be according to the corresponding relation of term keyword and theme, the Ontological concept word family of structure content, in order to promote the mark efficient of text, provide generalities retrieval based on domain body to content of text.
The present invention's advantage compared with prior art is: the corresponding relation that takes full advantage of subject body and key vocabularies, the loose train of thought between content is set up in association based on vocabulary, corresponding relation according to relative words, structure search core is searched for test to text, simultaneously take the matching content of search and associated data as starting point in similar perhaps concept carry out expanded search, obtain the guidance quality Query Result, thereby can effectively improve the inquiry quality.
Description of drawings
Fig. 1 is the block diagram of system of the present invention;
Fig. 2 is realization flow figure of the present invention;
Fig. 3 is search engine principle of work of the present invention;
Fig. 4 is the work schematic diagram of semantic editing machine of the present invention;
Fig. 5 is the schematic diagram that semantic editing machine of the present invention adds related vocabulary;
Fig. 6 is the schematic diagram of establishment new ideas word of the present invention;
Fig. 7 is the schematic diagram that derived concept word of the present invention is set to the XML form;
Fig. 8 is for utilizing configuration to carry out the schematic diagram of conceptual retrieval and meta-data extraction;
Fig. 9 is the schematic diagram that utilizes concept to retrieve of the present invention;
Figure 10 is the schematic diagram that utilizes concept to retrieve of the present invention;
Specific implementation method
As shown in Figure 1, the present invention mainly utilizes Java language, database, OWL processing engine and search engine technique to realize.
Self-defined concept vocabulary module: the core of concept vocabulary module is the vocabulary body editing and processing device that a Java language is write.The vocabulary ontology definition related between word family core vocabulary, subordinate vocabulary and concept vocabulary.Self-defined concept vocabulary module is built-in, and body concerns Processing Interface, user's words enter is converted into the OWL model with related modeling behavior, and waits for and deriving.The user can use the go forward side by side line correlation operation of this module of browser access, and data will be processed at the background java server.
The concept vocabulary is derived module: it is the module of carrying out exchanges data between user concept definition and semantic search engine that the concept vocabulary is derived module, the concept vocabulary is derived module user-defined notional word tree is exported as the structured document that uses XML to describe, this document namely can be stored in the local file system, also can be stored in the database simultaneously.
The conceptual index module is the module of text being carried out the generalities index process, when input text, namely can allow the user upload local file by network, also can carry out batch processing to the file under a certain storage space.This invocation of procedure JAVA file API carries out.Its submodule processing procedure is:
The search engine word-dividing mode: with Chinese word segmentation vocabulary memory-resident, scanning and internal memory vocabulary coupling by to input text are divided into Chinese vocabulary with the text of inputting, and relate to the identification of name, place name, unit name and the judgement of ambiguity vocabulary.The output word segmentation result.
The Conceptual Projection module: text vocabulary and conceptual model are mated, and the user can choice for use simple words matching algorithm, carry out the conceptual model coupling based on the matching algorithm of keyword and based on word frequency with existing degree of correlation algorithm.The simple words matching algorithm to the text participle after, mate with the vocabulary that forms behind whole participles and notional word and subordinate vocabulary, as long as identical vocabulary occurs, namely thinking has corresponding concepts to exist in the text; The keyword matching algorithm only mates keyword and concept vocabulary for extracting occurrence rate is the highest in the text front 10 effective vocabulary as keyword.Need to form word frequency with showing database based on word frequency by corpus with existing degree of correlation algorithm, namely form the word combination that often appears at simultaneously in the article, behind system's extracting keywords, not single keyword that uses carries out concept matching, also utilizes the relative words combination to shine upon simultaneously.
The conceptual index generation module: according to the concept vocabulary generating indexes file that the Conceptual Projection module forms, indexed file structure is: reference number of a document, key concept word string, document location link, associated description content etc.
The conceptual retrieval module still can use the participle engine that the non-standard word of user's input is carried out word segmentation processing, and result will be sent to the Conceptual Projection module and mate.
The result for retrieval module is extracted the content with the concept terminology match in the conceptual index file, and extract conceptual dependency word and content and mate and carry out highlighted demonstration, the result is presented in the user interface, and displaying contents has: conceptual dependency word in document location link, text-dependent metadata, text concept relative words, the text (blue highlighted), original term (red highlighted).
The conceptual navigation module shows the conceptional tree hierarchical, and the deployable conceptional tree of user also selects certain node to inquire about, and is the method for system supplymentary user inquiry.
Result set convergence module is that user's Query Result is all shown in the concept that different dimensions relates to, thereby the concept of a certain dimension of user selection limits Query Result, is a kind of method of system optimization Query Result.
Principal right requires module to be in the above description: concept definition module, concept derive module, Conceptual Projection module, conceptual index generation and retrieval module.
The concrete technology that the present invention relates to has: make up the content of text subject layer by the Ontological concept definition, the upper the next class that makes up between vocabulary by bulk process concerns, set up the Ontological concept data of text by the terminology match behind the participle, and pass through the Facet technology real concept retrieval of theme.
The traditional text describing method uses the thesaurus method to carry out the associated metadata explanation, and thesaurus is comprised of descriptor and descriptor Relations Among, adopts reference marks to show and also clearly distinguishes basic semantic relation between descriptor.Comprise following 3 kinds of relations in the thesaurus:
(1) identity relation (Equivalence Relationship) claims again the same relation, with generation relation, comprises synonym, nearly justice and concerns with generation, and this relation contains the relation that concept is identical or usage is identical.The announcement identity relation is conducive to increase access entry and according to the searching system needs the special finger degree of index and retrieval is controlled.
(2) hierarchical relationship (Hierarchical Relationship), claim again to belong to a minute relation, this relation comprises kind, the whole and multi-layer relation of belonging to, the hyponym of every kind of hierarchical relationship all must be identical with the concept type of hypernym, things, behavior or character in namely both must falling into the same category.The announcement hierarchical relationship helps by its expansion and dwindles seek scope, improves family's property retrieval capability.
(3) correlationship (Associative Relationship) claims again class edge relation, is to establish by index and retrieval angle a kind of relation that need to be mutually related.Correlationship is various main contacts between the announcement descriptor, enlarges range of search, carries out the important means that relevant information is searched.
Utilize bulk process, above descriptor method can be corresponded on the system platform and realize, utilize the XML file that lexical relation is described, realize in the related vocabulary in semantic editing machine, higher level's thesaurus and the relating subject vocabulary respectively such as above identity relation, hierarchical relationship, correlationship.
System obtains the related notion of text by the Auto-matching keyword, and these notional words are set to the metadata of text, increases simultaneously the property index of text.
System utilizes search engine to divide the region retrieval feature, can directly cross keyword, utilizes concept that text is classified or intelligent retrieval.
Fig. 2 has shown whole use procedure of the present invention, and is specific as follows:
(1) utilize semantic editing machine to create relevant body, the sign title of definition subject body.
(2) certain Ontological concept is increased related vocabulary, the term that can define the classification border should be chosen in related vocabulary, and editing machine can carry out additions and deletions to related vocabulary.
(3) the upper the next class that also can utilize semantic editing machine to set up between Ontological concept concerns or correlationship, can increase reference resources for Ontological concept.
(4) search engine is loaded into the subject body that defines in the internal memory to mate at any time calculating.
(5) text of wanting index is carried out participle.
(6) carry out word frequency statistics according to the participle situation.
(7) calculate the article keyword according to the word frequency situation.
(8) when index text, concept vocabulary and word family thereof and text participle are mated, when the match is successful, Ontological concept is added index data as property index.
(9) when it fails to match, the keyword that extracts is preserved.
(10) artificial screening keyword joins the concept vocabulary with neologisms.
(11) system can preserve the Ontological concept that extracts automatically as the article attribute, also can carry out conceptual retrieval by this attribute.
(12) user keys in inquiry string and retrieves.
(13) inquiry string of the user being keyed in carries out Conceptual Projection.
Retrieve by conceptual schema when (14) shining upon successfully.
(15) shine upon unsuccessfully and retrieve by the plain text matching way.
Fig. 3 has showed the working model of semantic search engine, is drawn into subject concept node coupling and storage from semantic key words, has also represented the process that the user utilizes search engine to retrieve simultaneously.Can find out from this figure, index and retrieval are the two-way process that index terms is stored and utilized.
Fig. 4 has showed the descriptor editing machine, can define the subject word, revises, imports, the operation such as derivation, can also define the relevant word family of descriptor, defines simultaneously the next class and related resource on it.
Fig. 5 has illustrated that the word that is the theme adds the process of related vocabulary.
Fig. 6 has illustrated the definition procedure of new descriptor.
Fig. 7 has showed the file structure of the thesaurus that defines.
Fig. 8 has showed the view of subject retrieval, for the content that retrieves, can further inquire about by each semantic level, such as: associative key, relevant grade and subject etc.
Fig. 9 has showed according to descriptor and has dwindled Query Result after the range of search.
Figure 10 has showed from another angle (grade) and has carried out Query Result after the concept convergence.
Claims (1)
1. the intelligent searching engine system of a semantic-based association is characterized in that comprising: self-defined concept vocabulary module, the derivation of concept vocabulary module, conceptual index module and conceptual retrieval module;
Self-defined concept vocabulary module: be a concept vocabulary editing machine, User Defined concept vocabulary is provided, set up interface related between vocabulary, described self-defined concept vocabulary functions of modules comprises the definition of key concept word, described key concept word is one group of sign vocabulary in the relevant word family, behind definition key concept word, for its subordinate vocabulary of a concept definition, related between related notion and concept, concept vocabulary editing machine is input as the user and wishes the conceptual system relative words that define, be output as the concept system that uses OWL to describe, described concept system comprises: the key concept word, conceptual dependency vocabulary, conceptual dependency relation and related resource finger URL;
The concept vocabulary is derived module: the module of carrying out exchanges data for self-defined concept vocabulary module and conceptual index intermodule, the structured document that it adopts XML to describe, user's defined notion vocabulary is exported as the structured document that uses XML to describe, use so that the conceptual index module to be provided;
The conceptual index module, that text is carried out the generalities index process, it is input as text, text is carried out concept to be extracted, notional word after the extraction forms index file, use in order in the future retrieval, it comprises following three submodules: the index text word-dividing mode: the text of input is divided into Chinese vocabulary, relate to the identification of name, place name, unit name and the judgement of ambiguity vocabulary, the basis of participle is the Chinese word segmentation vocabulary, and system will carry out cutting and vocabulary judgement to text by the definition in the participle vocabulary; Index Conceptual Projection module: text vocabulary and conceptual model are mated, with the conceptional tree structure memory-resident that is stored in the database, and mate with word segmentation result, the notional word string of output matching success, described coupling comprise the simple words matching algorithm, based on the matching algorithm of keyword and based on word frequency with existing degree of correlation algorithm; The conceptual index generation module: according to the concept vocabulary generating indexes file that index Conceptual Projection module forms, described index file content comprises: reference number of a document, key concept word string, document location link and associated description content;
The conceptual retrieval module, conceptual index is retrieved, the user shrinks by conceptional tree navigation, concept and range and search function realized in keyword, and described conceptual retrieval module comprises inquiry string word-dividing mode, query concept mapping block, conceptual retrieval as a result display module, concept guiding module, result set convergence module; The inquiry string word-dividing mode is carried out word segmentation processing to the non-standard word of user's input, and result will be sent to the query concept mapping block and mate; Conceptual retrieval is display module as a result, in the conceptual index file, extract the content with the concept terminology match, and extract conceptual dependency word and content and mate and carry out highlighted demonstration, the result is presented in the user interface, and displaying contents has: conceptual dependency word, original term in document location link, text-dependent metadata, text concept relative words, the text; The concept guiding module shows the conceptional tree hierarchical, and the deployable conceptional tree of user also selects certain node to inquire about, and is the method for system supplymentary user inquiry; Result set convergence module all shows user's Query Result in the concept that different dimensions relates to, thereby the concept of a certain dimension of user selection limits Query Result.
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| CN109670102B (en) * | 2018-12-29 | 2023-07-28 | 北京神舟航天软件技术有限公司 | User retrieval intention judging method based on word list model |
| US10489454B1 (en) | 2019-06-28 | 2019-11-26 | Capital One Services, Llc | Indexing a dataset based on dataset tags and an ontology |
| US11531703B2 (en) | 2019-06-28 | 2022-12-20 | Capital One Services, Llc | Determining data categorizations based on an ontology and a machine-learning model |
| CN113793193B (en) * | 2021-08-13 | 2024-02-02 | 唯品会(广州)软件有限公司 | Data search accuracy verification method, device, equipment and computer readable medium |
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