CN104750836B - Digital publication semantic tagger optimization method based on ontology - Google Patents
Digital publication semantic tagger optimization method based on ontology Download PDFInfo
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Abstract
The present invention provides a kind of digital publication semantic tagger optimization method based on ontology.Include the following steps: that document content pre-processes；Construct ontology model；Building individual simultaneously fills data attribute value；Adjust document marking and mark weight；Storage mark and mark weight；It inputs word and carries out knowledge query, matched data simultaneously sorts by weight.The method of the present invention can be improved the accuracy of document marking, and user can faster search effective document when carrying out knowledge query using ontology knowledge base, and can improve the accuracy of other associated electronic document markings.
Description
Technical field
The present invention relates to digital publication technical field more particularly to a kind of digital publication semanteme marks based on ontology
Infuse optimization method.
Background technique
Knowledge processing is the inexorable trend of Information Technology Development, and with higher and higher to knowledge application requirement, traditional knows
Know Database Systems and be no longer satisfied new demand, so ontology is referred in knowledge engineering, by ontology relative theory skill
Art applies in the exploitation of knowledge base.
Ontology knowledge system is later period the 1970s, expert system, knowledge system and knowledge-intensive information system
The constructing technology of system develops and forms knowledge engineering, and the system established is referred to as knowledge system (knowledge-based
Systems).Knowledge system is the most important industrialization of artificial intelligence subject and commercialization product.Knowledge system is for assisting people
Carry out problem solving, such as detect credit card fraud, accelerate Ship Design, assisted medical diagnosis, keep scientific software more intelligent
Change, financial service, the evaluation of product quality and advertising are provided to F/O, support the service of electric network extensive
It is multiple.
With the continuous development of digital publishing, the explosive growth of modern the Internet digital content resource also goes out at this stage
The content for having showed some pairs of electronic publications refines the technology of mark, but these are according to basic word to the extraction of content mark
Library and content context extract.The notation methods of this extraction are not bound with the domain background of publication, have very much
The relevant key message in field is filtered, and reduces the accuracy for being labeled in specific area；So that mark cannot completely represent
The core and main contents of document.
When the information to the field is retrieved according to mark, can make to have in terms of information recall ratio and precision ratio very big
Shortcoming, does not make full use of content markup information, and the relationship and structure between information also do not show adequately, so that with
Family needs to take a significant amount of time on information sifting.
Summary of the invention
The technical problem to be solved by the present invention is to the above-mentioned technical deficiencies of face, and providing one kind can be improved document
The accuracy of mark, user can faster search effective document, and energy when carrying out knowledge query using ontology knowledge base
Improve the digital publication semantic tagger optimization method based on ontology of the accuracy of other associated electronic document markings.
The technical solution adopted by the present invention to solve the technical problems is:
Digital publication semantic tagger optimization method based on ontology, which comprises the steps of:
Document content pretreatment: in computer systems parsing document, is extracted and is closed using keyword extraction tool
Key word, and the weight for calculating keyword based on word position, provide data basis for subsequent builds individual.
Construct ontology model: ontology is the specific expression of the formalization to the class in some field, the characteristic of each class
The various aspects of class and its characteristic of constraint and attribute are described, therefore ontology includes class, object properties and data attribute.It is interior
Holding mark optimization method is realized based on ontology, constructs ontology by ontology construction tool in computer systems, using certainly
Downward methodological principle is pushed up, completes the building of class, object properties, data attribute in the tool.
Building individual simultaneously fills data attribute value: individual is the example established according to class existing in ontology, building individual
It is the process that user models document according to document content；When filling individual data items attribute information, in each data category
Property a corresponding text box, for inputting and showing the data attribute information；The value of data attribute is obtained from document marking
, by marking the attribute value that can not be obtained key message will be obtained as data attribute value according to full-text search.
It adjusts document marking and marks weight: obtaining the attribute value of the original markup information of document and above-mentioned individual filling
Document corresponding with attribute value, is adjusted the mark in document；According to the rank of class where individual and data attribute
Priority is to attribute value setting weight and as the new mark of document, if attribute value is the original mark of document, original weight
With existing weight number combining, then new and old mark is sorted according to weight, the mark for selecting weight high as document.
Storage mark and mark weight: deleting the corresponding original mark of document, will mark and weight adjusted among the above
It stores in the corresponding tables of data of mark；When other documents carry out content mark, the data in table are marked as impact factor
It is added in the weight computing formula of mark.
It inputting word and carries out knowledge query, matched data simultaneously sorts by weight: user is inquired by knowledge query, when
Individual is matched according to data attribute information, it can be according to the power of lookup attribute value in a document when showing all information of the individual
Value is ranked up, and display result can arrange query result according to the descending of weight.
The beneficial effects of the present invention are:
The weight marked in digital publication is calibrated by individual attribute information in ontology, improves the standard of document marking
True property, user can faster search effective document when carrying out knowledge query using ontology knowledge base；
The impact factor of weight computing formula in mark will be extracted as other documents by the mark after optimization, improved
The accuracy of other electronic document markings.
The beneficial effects of the present invention are:
Markup information can be provided to digital publication to check, realize band mark preview and the reading side of digital publication
Formula, can help reader the subject information quickly and effectively checked in document.
Conceptual network can be established between electronic document simultaneously, the foundation of ontology library provides effective data supporting.
Detailed description of the invention
Fig. 1 is the flow chart of the embodiment of the present invention.
Specific embodiment
With reference to embodiment, the present invention is further illustrated:
Digital publication semantic tagger optimization method based on ontology as shown in Figure 1, which is characterized in that including walking as follows
It is rapid:
Document content pretreatment: in computer systems parsing document, is extracted and is closed using keyword extraction tool
Key word, and the weight of keyword is calculated based on word position, data basis is provided for subsequent builds individual.
Construct ontology model: ontology is the specific expression of the formalization to the class in some field, the characteristic of each class
The various aspects of class and its characteristic of constraint and attribute are described, therefore ontology includes class, object properties and data attribute.It is interior
Holding mark optimization method is realized based on ontology, on condition that needing to construct ontology by ontology construction tool, we are using certainly
Downward methodological principle is pushed up, completes the building of class, object properties, data attribute in the tool.
Building individual simultaneously fills data attribute value: individual is the example established according to class existing in ontology, building individual
It is the process that user models document according to document content；When filling individual data items attribute information, in each data category
Property a corresponding text box, for inputting and showing the data attribute information；The value of data attribute is obtained from document marking
, by marking the attribute value that can not be obtained key message will be obtained as data attribute value according to full-text search.
It adjusts document marking and marks weight: obtaining the attribute value of the original markup information of document and above-mentioned individual filling
Document corresponding with attribute value, is adjusted the mark in document；By the excellent of the rank of class where individual and data attribute
First grade is added in weight computing formula as weight, obtains the weight of attribute value and the new mark as document, will be new
Old mark is according to the high mark of weight sequencing selection weight and as the new mark of document.If attribute value is the original mark of document
Note, then original weight and existing weight number combining, then new and old mark is sorted, select weight high as document according to weight
Mark.
Storage mark and mark weight: deleting the corresponding original mark of document, will mark and weight adjusted among the above
It stores in the corresponding tables of data of mark；When other documents carry out content mark, the data in table are marked as impact factor
It is added in the weight computing formula of mark.
It inputting word and carries out knowledge query, matched data simultaneously sorts by weight: user is inquired by knowledge query, when
Individual is matched according to data attribute information, it can be according to the power of lookup attribute value in a document when showing all information of the individual
Value is ranked up, and display result can arrange query result according to the descending of weight.
Protection scope of the present invention is not limited to the above embodiments, it is clear that those skilled in the art can be to this hair
It is bright to carry out various changes and deformation without departing from scope and spirit of the present invention.If these changes and deformation belong to power of the present invention
In the range of benefit requirement and its equivalent technologies, then including the intent of the present invention also includes these changes and deforms.
Claims (1)
1. the digital publication semantic tagger optimization method based on ontology, which comprises the steps of:
Document content pretreatment: in computer systems parsing document, extracts keyword using keyword extraction tool,
And the weight of keyword is calculated based on word position, data basis is provided for subsequent builds individual；
It constructs ontology model: ontology being constructed by ontology construction tool in computer systems, it is former using top-down method
Then, the building of class, object properties, data attribute is completed in the tool, and composition includes the sheet of class, object properties and data attribute
Body；
Building individual simultaneously fills data attribute value: individual is the example established according to class existing in ontology, and building individual is to use
The process that family models document according to document content, and individual data items attribute is filled, data are obtained from document marking
The value of attribute；
It adjusts document marking and marks weight: obtaining the attribute value and category of the original markup information of document and above-mentioned individual filling
Property the corresponding document of value, the mark in document is adjusted, by the rank of class where individual and the priority of data attribute
It is added in weight computing formula as weight, obtains the weight of attribute value and the new mark as document；
Storage mark and mark weight: deleting the corresponding original mark of document, will mark and weight storage adjusted among the above
Into the corresponding tables of data of mark；When other documents carry out content mark, the data marked in table are added as impact factor
Into the weight computing formula of mark；
It inputs word and carries out knowledge query, matched data simultaneously sorts by weight: user is inquired by knowledge query, works as basis
Data attribute information matching individual, when showing all information of the individual can according to lookup attribute value weight in a document into
Row sequence, display result can arrange query result according to the descending of weight；
The described building individual is simultaneously filled in data attribute value, in the corresponding text box of each data attribute, for inputting and
Show the data attribute information；The value of data attribute is obtained from document marking, by marking the attribute value that can not be obtained
Key message will be obtained according to full-text search as data attribute value；
In the adjustment document marking and mark weight, if attribute value is the original mark of document, original weight and existing
Then weight number combining sorts new and old mark according to weight, the mark for selecting weight high as document.
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Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103593335A (en) * | 2013-09-05 | 2014-02-19 | 姜赢 | Chinese semantic proofreading method based on ontology consistency verification and reasoning |
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| US20060277176A1 (en) * | 2005-06-01 | 2006-12-07 | Mydrew Inc. | System, method and apparatus of constructing user knowledge base for the purpose of creating an electronic marketplace over a public network |
| KR100966651B1 (en) * | 2008-01-16 | 2010-06-29 | 재단법인서울대학교산학협력재단 | Ontology-based Semantic Annotation System and Method thereof |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103593335A (en) * | 2013-09-05 | 2014-02-19 | 姜赢 | Chinese semantic proofreading method based on ontology consistency verification and reasoning |
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| Title |
|---|
| 关联数据和知识表示的自动语义标注技术;谢铭;《中国博士学位论文全文数据库 信息科技辑》;20130615(第06期);I138-65 |
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Inventor after: Liu Yongjian Inventor after: Bai Lihua Inventor after: Wei Min Inventor after: Wu Lei Inventor after: Yang Chaoyang Inventor after: Zeng Rui Inventor after: Li Wenzhong Inventor after: Yang Hui Inventor before: Liu Yongjian Inventor before: Bai Lihua Inventor before: Yang Chaoyang Inventor before: Zeng Rui Inventor before: Li Wenzhong Inventor before: Yang Hui |