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Intelligent Retrieval Research Based On Tea Domain Ontology

Posted on:2011-02-27Degree:MasterType:Thesis
Country:ChinaCandidate:L J ZhuFull Text:PDF
GTID:2198330332462226Subject:Computer application technology
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Since 1990s, information retrieval is developing to the direction of domain and intelligence. Improving the quality of search and promoting a satisfactory retrieval technology became the front and hotspot of the world-wide. Ontology as a conceptual model of the expression of knowledge sharing has increasingly become knowledge engineering, knowledge management, information retrieval and semantic Web and other important component of the field. Particularly since the semantic Web has been proposed, Semantic Web technology based on ontology is becoming an important research direction of the field of artificial intelligence and knowledge engineering in domain intelligence retrieval. This research has far-reaching significance in the acquisition,express,analysis and application of knowledge.With the tea pest knowledge as the research object and the emphasis of the research is about the two key issues of tea domain ontology semantic retrieval—tea domain ontology logic checking issue and tea domain ontology intelligent retrieval issue. The paper researches the logic checking method of tea domain ontology knowledge based on Description Logics,and designed the intelligent retrieval model based on tea domain ontology. The research checked the logic of tea domain ontology, ensured the logical correctness of tea ontology to prepare for the intelligent retrieval. Through intelligent retrieval research of tea domain ontology, produced intelligent navigation links, and enhanced the accuracy of information retrieval.This study mainly in the following areas:1) The logic checking method research of domain ontology knowledge based on Description Logics. Logic checking of domain ontology knowledge based on description logics mainly classified as judging concept satisfiability on TBox and judging ABox consistency on TBox. The paper adopts Pellet reasoner of Tableaux algorithm-based logic checking on tea pest ontology base, verifying the validity and completeness of the ontology logic definition.2) The model and method research of intelligent retrieval based on tea domain ontology. Intelligent retrieval model based on tea pest ontology include: inference engine and category navigation. Inference engine use XML DOM and Xpath technical analysis to analyze tea pest ontology concepts and concepts semantic relationships, and to reason out of synonyms for the new query of dynamic combination. Category navigation infer the synonymous words, border terms, narrower terms and related terms and so on, and call the Google Ajax Search API links to WWW search results. It Infers all related concepts nodes by keywords the user entered, which express the hierarchy of concept by classifying, and form a link for users to search.3)The design and realization of intelligent retrieval system based on tea pest ontology. The intelligent retrieval system can collect and retrieve information with a limited subject, and display the concept of hierarchy with graphical form, and the link of intelligent navigation can be produced to retrieve information under the scope of the most relevant subject. It upgrades the information retrieval from the level of keywords-based into the level of knowledge-based. The system can help users to find the interesting information more easily, and enhance the quality of information services.The research laid the groundwork for the intelligent retrieval of agriculture domain ontology, the establishment of agricultural semantic networks, full sharing and reuse of agricultural domain knowledge. But the bulk of Intelligent Search is a complicated systematic project, involving multiple disciplines, due to time and personnel constraints, so this work is still very limited. The prototype tool is still in the experimental stage of exploration work .The future work still need further expansion and in-depth study.
Keywords/Search Tags:Tea Pest Ontology, Description Logic, Logic Checking, Concept matching, Ontology Retrieval
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