ABSTRACT
Semantic Web shows us the potential infrastructure of the next generation Web. Web information will be understandable to machines in this infrastructure. Our research work has the aim of embedding machine-understandable semantic information in ordinary HTML files automatically given the domain ontology. We focus on automatically acquiring the instances of concepts and relations defined in domain ontology from Web pages. This paper describes how to recognize the relation between Web pages (a kind of relation instance) by using Artificial Neural Network (ANN). The input vector of ANN is determined by the type of Web pages, the number and type of hyperlinks between the Web pages, and the similarity in Web pages' contents. We also show the initial results got by our prototype system.
- Tom M. Mitchell. Machine Learning. The McGraw-Hill Companies, Inc. 1997. Google ScholarDigital Library
- Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom Mitchell, Kamal Nigam, Sean Slattery. Learning to Extract Symbolic Knowledge from the World Wide Web. Proceedings of 15th National Conference on Artificial Intelligence (AAAI-98). Google ScholarDigital Library
- Jeffrey Dean, Monika R. Henzinger. Finding Related Pages in the World Wide Web. In Proceedings of 8th International World-Wide Web Conference, May 1999. Google ScholarDigital Library
- Neel Sundaresan, Jeonghee Yi. Mining the Web for relations. In the 9th International WWW Conference, May 2000. Google ScholarDigital Library
- J. Kleinberg. Authoritative Sources in a Hyperlinked Environment. In Proceedings of 9th ACM-SIAM Symposium on Discrete Algorithms, May 1997. Google ScholarDigital Library
- Nicola Guarino. Formal Ontology and Information Systems. In Proceedings of FOIS'98, June 1998.Google Scholar
- Kamal Nigam, Rayid Ghani. Analyzing the Effectiveness and Applicability of Co-training. In the 9th International Conference on Information and Knowledge Management. Google ScholarDigital Library
- Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom Mitchell. Text Classification from Labeled and Unlabeled Documents using EM. the Machine Learning Journal. 2000. Google ScholarDigital Library
- Kushmerick, N.; Weld, D.; and Doorenbos, R. Wrapper induction for information extraction. In Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI).Google Scholar
- Monika R. Henzinger. Hyperlink Analysis for the Web. IEEE Internet Computing. 2001. Google ScholarDigital Library
- George A. Miller. WordNet: An On-line Lexical Database. In the International Journal of Lexicography, Vol.3, No.4, 1990.Google Scholar
- Soumen Chakrabarti, Martin van den Berg, and Byron Dom. Focused crawling: A new approach to topic-specific Web resource discovery. In the 8th International World Wide Web Conference, 1999. Google ScholarDigital Library
- Resource Description Framework(RDF) Model and Syntax Specification http://www.w3.org/TR/REC-rdf-syntax/1.Google Scholar
- Resource Description Framework(RDF) Schema Specification 1.0 http://www.w3.org/TR/rdf-schema/Google Scholar
- Semantic Web http://www.semanticWeb.org/introduction.htmlGoogle Scholar
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