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The Research of Deep Web Uncertain Schema Matching Based on Domain Ontology

Author: GaoHuaLing
Tutor: WangJu
School: Guangxi Normal University
Course: Computer Software and Theory
Keywords: Deep Web Domain ontology Similarity Schema matching uncertainty
CLC: TP393.09
Type: Master's thesis
Year: 2011
Downloads: 20
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Abstract

With the continuous development of Internet technology, more and more network information resources keep appearing, there goes the problem how to utilize the resources which caused the concern of the majority of Internet users and academic researchers’attention. According to the distribution of the information resources, Web position and feature can be classified as Surface Web and Deep Web two parts. The traditional search engines can only retrieve Surface Web information, and cannot crawl the more informative, better qualified and more specific, stronger-structured Deep Web database information effectively.Deep Web information integration is the important means to use Deep Web information resources effectively. Deep Web querying interface integrated research is the core of information integration, which plays the important "transitional" role. The current inquires the interface integration has some problems:Chinese semantic calculation is not accurate enough, inquires interface schema matching method is complex, time complexity is relatively huge, lack of consideration in schema matching uncertainty, and etc. According to these shortcomings and the insufficiencies, this paper proposes a method based on the domain ontology querying interface integration method, this method is a kind of whole matching method, which has broken the low efficient bottleneck in traditional two-two matching approach, greatly simplified the complex matching process. Meanwhile put forward a kind of selection criteria for uncertainty selection, opened up new ideas to the research of uncertain matching. This main research work and contributions of the paper can be summed up as follows:(1) This paper mainly introduces ontology and analyzes the structure of domain ontology, according to the method of constructing domain ontology and combining related Deep Web querying interface properties and real case in tourism field uses more standard, expressive ontology language OWL2 as code language, constructs query-oriented interface tourism domain ontology.(2) Based on thorough study and analysis of the traditional schema matching technology, this paper presents a querying interface schema matching method based on the Deep Web domain ontology, using this method, it is possible to realize the holistic matching in specific areas, which is much better than the traditional pairwise matching in efficiency. The method makes full use of ontology concept and the semantic relations between concepts realized the understanding of querying interface in the semantic level.(3) For the most important problem similarity calculation in schema matching, this paper proposes an improved attribute similarity calculating method. The method was applied to the Chinese query interface integration mode matching problem, considering the rules and characteristics in Chinese query interface, improved the Chinese semantic similarity calculation formula based on HowNet. Experiments evidence showed that this formula can greatly improve the calculation accuracy.(4) This paper proposes the idea that to base on the attribute location to evaluate the attribute matching credibility in relating to the uncertainty schema matching, and gives the quantitative calculation formula of matching credibility, which can to help us choose more reasonable matching results.(5) This paper realized the integration system based on the ontology-based querying interface, including noumenon management module, pretreatment module for inquires interface, similarity calculation module, schema matching generation module and query interface integration module. Assessd and proved the key technology and calculation method proposed in this paper, provides a good platform for the collection of experimental result data.Finally, through the established system platform, set the corresponding experiment, then analyzed and evaluated the experimental results, proved the attribute matching method based on ontology and the accuracy of the improved similarity calculation method.

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