CN110377700A - A kind of professional knowledge semantic retrieval system - Google Patents
A kind of professional knowledge semantic retrieval system Download PDFInfo
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- CN110377700A CN110377700A CN201910583098.0A CN201910583098A CN110377700A CN 110377700 A CN110377700 A CN 110377700A CN 201910583098 A CN201910583098 A CN 201910583098A CN 110377700 A CN110377700 A CN 110377700A
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- 238000005457 optimization Methods 0.000 claims abstract description 10
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Abstract
A kind of professional knowledge semantic retrieval system, including application layer, Business Logic and data Layer；Application layer includes input module and output module；Business Logic includes that data processing module, information transfer module and mark module；Data Layer includes several Knowledge Base Modules.The present invention carries out fuzzy concept conversion to the search condition received, query optimization extension, and generate synonymous search condition collection and semantic similarity search condition collection, then participle parsing is carried out to each search condition collection, Ontology query language is converted by parsing result, then Ontology query language professional knowledge to relevant Knowledge Base Module is matched, correction, it is finally transferred in information bank and the matched triple of Ontology query language, the knowledge that individual consumer retrieved is marked in mark module simultaneously, and it stores it in information bank, substantially increase the precision of semantic retrieval and the speed of retrieval, facilitate study of the user to professional knowledge.
Description
Technical field
The present invention relates to knowledge learning field more particularly to a kind of professional knowledge semantic retrieval systems.
Background technique
In recent years, in order to keep up with the paces of work, more and more units start to pay attention to study of the employee to knowledge, product
Various knowledge learning activities are carried out in pole, evoke the learning enthusiasm of employee, however employee is commonly encountered during learning knowledge
More difficult semanteme affects the efficiency and effect of employee's study.
To solve the above problems, proposing a kind of professional knowledge semantic retrieval system in the application.
Summary of the invention
(1) goal of the invention
To solve technical problem present in background technique, the present invention proposes a kind of professional knowledge semantic retrieval system, this
Invention retrieves professional knowledge semanteme by constructing the system including application layer, Business Logic and data Layer, retrieval behaviour
Make simple, data processing module therein, information transfer module and mark module mating reaction, to the search condition received into
The conversion of row fuzzy concept, query optimization extension, and synonymous search condition collection and semantic similarity search condition collection are generated, then to each
Search condition collection carries out participle parsing, Ontology query language is converted by parsing result, then by Ontology query language and information
Relevant professional knowledge is matched, is corrected in library module, is finally transferred in information bank and Ontology query language matched three
Tuple, while the knowledge that individual consumer retrieved is marked in mark module, and stores it in information bank, greatly improves
The precision of semantic retrieval and the speed of retrieval, facilitate study of the user to professional knowledge.
(2) technical solution
To solve the above problems, the present invention provides a kind of professional knowledge semantic retrieval system, including application layer, business are patrolled
Collect layer and data Layer；Application layer includes input module and output module；Business Logic includes that data processing module, information are transferred
Module and mark module；Data Layer includes several Knowledge Base Modules；Input module, for search interface with list, query word,
The form of natural language inputs search condition；Output module is used for feedback searching result；Data processing module, for reception
The search condition arrived carries out fuzzy concept conversion, and completes query optimization extension, and synonymous search condition collection and language are generated after extension
The close search condition collection of justice, carries out participle parsing to each search condition collection, and convert Ontology query language for parsing result, wraps
Include expanding element, resolution unit, format conversion unit；Information transfers module, for obtaining Ontology query language, by Ontology Query
Language professional knowledge to relevant Knowledge Base Module is matched, is corrected, and is transferred in information bank and Ontology query language
Matched triple, including similarity retrieval unit, matching degree retrieval unit, information correction unit and transfer unit；Identify mould
Block for the knowledge that individual consumer retrieved to be marked, and stores it in information bank；Knowledge Base Module, for depositing
Store up professional knowledge, including document library unit, network connection unit, administrative unit and individual character document element.
Preferably, information correction unit, for detecting whether the value for transferring the semantic attribute of the professional knowledge in result has
Ambiguity handles data, if it is not, then knot will be retrieved finally if it is, correction result is sent to data processing module again
Fruit is transmitted to output module.
Preferably, administrative unit updates professional knowledge for loading, and to information bank by way of increasing, changing, delete, look into
In original professional knowledge be managed.
Preferably, individual character document element, for carrying out intelligent semantic retrieval for individual consumer.
Preferably, data processing module handles search condition using filtering technique.
Preferably, Ontology query language is Web Ontology Language OWL.
Preferably, the work step of above system includes:
S1, building include the professional knowledge semantic retrieval system of application layer, Business Logic and data Layer；
S2, professional knowledge is collected, and its structuring is handled, establish information bank；
S3, user input list, query word or natural language by search interface, propose retrieval request；
S4, data processing module carry out fuzzy concept conversion to the search condition received, and complete query optimization extension,
Synonymous search condition collection and semantic similarity search condition collection are generated after extension, participle parsing are carried out to each search condition collection, and will
Parsing result is converted into Ontology query language；
S5, information transfer module and obtain Ontology query language, to relevant Knowledge Base Module specially by Ontology query language
Industry knowledge is matched, is corrected, and is transferred and the matched triple of Ontology query language in information bank；
S6, mark module are marked the knowledge that individual consumer retrieved, and store it in information bank；
S7, search interface are to user feedback search result.
Above-mentioned technical proposal of the invention has following beneficial technical effect:
In the present invention, by construct include application layer, Business Logic and data Layer system to professional knowledge semanteme into
Row retrieval, search operaqtion is simple, and data processing module therein, information transfer module and mark module mating reaction, to reception
The search condition arrived carries out fuzzy concept conversion, query optimization extension, and generates synonymous search condition collection and semantic similarity retrieval
Condition set, then carries out participle parsing to each search condition collection, Ontology query language is converted by parsing result, then by ontology
Query language professional knowledge to relevant Knowledge Base Module is matched, is corrected, and is finally transferred in information bank and is looked into ontology
The matched triple of language is ask, while the knowledge that individual consumer retrieved is marked in mark module, and stores it in letter
It ceases in library, substantially increases the precision of semantic retrieval and the speed of retrieval, facilitate study of the user to professional knowledge.
Detailed description of the invention
Fig. 1 is a kind of structural framing figure of professional knowledge semantic retrieval system proposed by the present invention.
Specific embodiment
In order to make the objectives, technical solutions and advantages of the present invention clearer, With reference to embodiment and join
According to attached drawing, the present invention is described in more detail.It should be understood that these descriptions are merely illustrative, and it is not intended to limit this hair
Bright range.In addition, in the following description, descriptions of well-known structures and technologies are omitted, to avoid this is unnecessarily obscured
The concept of invention.
As shown in Figure 1, a kind of professional knowledge semantic retrieval system proposed by the present invention, including application layer, Business Logic
And data Layer；Application layer includes input module and output module；Business Logic includes that data processing module, information transfer module
And mark module；Data Layer includes several Knowledge Base Modules；Input module is used in search interface with list, query word, nature
The form of language inputs search condition；Output module is used for feedback searching result；Data processing module, for receiving
Search condition carries out fuzzy concept conversion, and completes query optimization extension, and synonymous search condition collection and semantic phase are generated after extension
Nearly search condition collection carries out participle parsing to each search condition collection, and converts Ontology query language for parsing result, including expand
Open up unit, resolution unit, format conversion unit；Information transfers module, for obtaining Ontology query language, by Ontology query language
The professional knowledge to relevant Knowledge Base Module is matched, is corrected, and is transferred in information bank and matched with Ontology query language
Triple, including similarity retrieval unit, matching degree retrieval unit, information correction unit and transfer unit；Mark module is used
It is marked, and is stored it in information bank in the knowledge that individual consumer retrieved；Knowledge Base Module, for storing profession
Knowledge, including document library unit, network connection unit, administrative unit and individual character document element.
In an alternative embodiment, information correction unit, for detecting the professional knowledge semanteme category transferred in result
Property value whether have ambiguity, if it is, will correction result be sent to data processing module, handle data again, if not,
Final search result is then transmitted to output module.
In an alternative embodiment, administrative unit updates professional knowledge for loading, and by increasing, changing, deleting, looking into
Mode professional knowledge original in information bank is managed.
In an alternative embodiment, individual character document element, for carrying out intelligent semantic retrieval for individual consumer.
In an alternative embodiment, data processing module handles search condition using filtering technique.
In an alternative embodiment, Ontology query language is Web Ontology Language OWL.
In an alternative embodiment, the work step of above system includes:
S1, building include the professional knowledge semantic retrieval system of application layer, Business Logic and data Layer；
S2, professional knowledge is collected, and its structuring is handled, establish information bank；
S3, user input list, query word or natural language by search interface, propose retrieval request；
S4, data processing module carry out fuzzy concept conversion to the search condition received, and complete query optimization extension,
Synonymous search condition collection and semantic similarity search condition collection are generated after extension, participle parsing are carried out to each search condition collection, and will
Parsing result is converted into Ontology query language；
S5, information transfer module and obtain Ontology query language, to relevant Knowledge Base Module specially by Ontology query language
Industry knowledge is matched, is corrected, and is transferred and the matched triple of Ontology query language in information bank；
S6, mark module are marked the knowledge that individual consumer retrieved, and store it in information bank；
S7, search interface are to user feedback search result.
In the present invention, by construct include application layer, Business Logic and data Layer system to professional knowledge semanteme into
Row retrieval, search operaqtion is simple, and data processing module therein, information transfer module and mark module mating reaction, to reception
The search condition arrived carries out fuzzy concept conversion, query optimization extension, and generates synonymous search condition collection and semantic similarity retrieval
Condition set, then carries out participle parsing to each search condition collection, Ontology query language is converted by parsing result, then by ontology
Query language professional knowledge to relevant Knowledge Base Module is matched, is corrected, and is finally transferred in information bank and is looked into ontology
The matched triple of language is ask, while the knowledge that individual consumer retrieved is marked in mark module, and stores it in letter
It ceases in library, substantially increases the precision of semantic retrieval and the speed of retrieval, facilitate study of the user to professional knowledge.
It should be understood that above-mentioned specific embodiment of the invention is used only for exemplary illustration or explains of the invention
Principle, but not to limit the present invention.Therefore, that is done without departing from the spirit and scope of the present invention is any
Modification, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.In addition, appended claims purport of the present invention
Covering the whole variations fallen into attached claim scope and boundary or this range and the equivalent form on boundary and is repairing
Change example.
Claims (7)
1. a kind of professional knowledge semantic retrieval system, which is characterized in that including application layer, Business Logic and data Layer；Using
Layer includes input module and output module；Business Logic includes that data processing module, information transfer module and mark module；Number
It include several Knowledge Base Modules according to layer；
Input module, for inputting search condition in the form of list, query word, natural language in search interface；
Output module is used for feedback searching result；
Data processing module for carrying out fuzzy concept conversion to the search condition received, and is completed query optimization extension, is expanded
Synonymous search condition collection and semantic similarity search condition collection are generated after exhibition, participle parsing are carried out to each search condition collection, and will solution
Analysis result is converted into Ontology query language, including expanding element, resolution unit, format conversion unit；
Information transfers module, for obtaining Ontology query language, by Ontology query language profession to relevant Knowledge Base Module
Knowledge is matched, is corrected, and is transferred and the matched triple of Ontology query language, including similarity retrieval list in information bank
Member, matching degree retrieval unit, information correction unit and transfer unit；
Mark module for the knowledge that individual consumer retrieved to be marked, and stores it in information bank；
Knowledge Base Module, for storing professional knowledge, including document library unit, network connection unit, administrative unit and individual character text
Shelves unit.
2. a kind of professional knowledge semantic retrieval system according to claim 1, which is characterized in that information correction unit is used
Whether there is ambiguity in the value that the professional knowledge semantic attribute in result is transferred in detection, if it is, correction result is sent to
Data processing module handles data again, if it is not, then final search result is transmitted to output module.
3. a kind of professional knowledge semantic retrieval system according to claim 1, which is characterized in that administrative unit, for adding
It carries and updates professional knowledge, and professional knowledge original in information bank is managed by way of increasing, changing, delete, look into.
4. a kind of professional knowledge semantic retrieval system according to claim 1, which is characterized in that individual character document element is used
In for individual consumer carry out intelligent semantic retrieval.
5. a kind of professional knowledge semantic retrieval system according to claim 1, which is characterized in that data processing module uses
Filtering technique handles search condition.
6. a kind of professional knowledge semantic retrieval system according to claim 1, which is characterized in that Ontology query language is
Web Ontology Language OWL.
7. a kind of professional knowledge semantic retrieval system according to claim 1, which is characterized in that the work step of the system
Include:
S1, building include the professional knowledge semantic retrieval system of application layer, Business Logic and data Layer；
S2, professional knowledge is collected, and its structuring is handled, establish information bank；
S3, user input list, query word or natural language by search interface, propose retrieval request；
S4, data processing module carry out fuzzy concept conversion to the search condition received, and complete query optimization extension, extension
After generate synonymous search condition collection and semantic similarity search condition collection, participle parsing carried out to each search condition collection, and will parsing
As a result it is converted into Ontology query language；
S5, information transfer module and obtain Ontology query language, and Ontology query language profession to relevant Knowledge Base Module is known
Knowledge is matched, is corrected, and is transferred and the matched triple of Ontology query language in information bank；
S6, mark module are marked the knowledge that individual consumer retrieved, and store it in information bank；
S7, search interface are to user feedback search result.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113553399A (en) * | 2021-07-16 | 2021-10-26 | 山东建筑大学 | Text search method and system based on fuzzy language approximate concept lattice |
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| US20130013645A1 (en) * | 2011-07-08 | 2013-01-10 | First Retail Inc. | Semantic matching |
| CN103392177A (en) * | 2011-02-25 | 2013-11-13 | 英派尔科技开发有限公司 | Ontology expansion |
| CN103886099A (en) * | 2014-04-09 | 2014-06-25 | 中国人民大学 | Semantic retrieval system and method of vague concepts |
| CN107045545A (en) * | 2017-03-30 | 2017-08-15 | 山东省农业科学院 | A kind of peanut cultivation information database constructing system |
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- 2019-07-01 CN CN201910583098.0A patent/CN110377700A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101373532A (en) * | 2008-07-10 | 2009-02-25 | 昆明理工大学 | FAQ Chinese request-answering system implementing method in tourism field |
| CN103392177A (en) * | 2011-02-25 | 2013-11-13 | 英派尔科技开发有限公司 | Ontology expansion |
| US20130013645A1 (en) * | 2011-07-08 | 2013-01-10 | First Retail Inc. | Semantic matching |
| CN103886099A (en) * | 2014-04-09 | 2014-06-25 | 中国人民大学 | Semantic retrieval system and method of vague concepts |
| CN107045545A (en) * | 2017-03-30 | 2017-08-15 | 山东省农业科学院 | A kind of peanut cultivation information database constructing system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113553399A (en) * | 2021-07-16 | 2021-10-26 | 山东建筑大学 | Text search method and system based on fuzzy language approximate concept lattice |
| CN113553399B (en) * | 2021-07-16 | 2022-05-27 | 山东建筑大学 | Text search method and system based on fuzzy language approximate concept lattice |
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Application publication date: 20191025 |