Taxonomic Semantic Indexing for Textual Case-Based Reasoning
Case-Based Reasoning (CBR) solves problems by reusing past problem-solving experiences maintained in a casebase. The key CBR knowledge container therefore is its casebase. However there are further containers such as similarity, reuse and revision knowledge that are also crucial. Automated acquisition approaches are particularly attractive to discover knowledge for such containers. Majority of research in this area is focused on introspective algorithms to extract knowledge from within the casebase. However the rapid increase in Web applications has resulted in large volumes of user generated experiential content. This forms a valuable source of background knowledge for CBR system development. In this paper we present a novel approach to acquiring knowledge from Web pages. The primary knowledge structure is a dynamically generated taxonomy which once created can be used during the retrieve and reuse stages of the CBR cycle. Importantly this taxonomy is pruned according to a clustering-based sense disambiguation heuristic that uses similarity over the solution vocabulary of cases. Algorithms presented in the paper are applied to several online FAQ systems consisting of textual problem-solving cases. The goodness of generated taxonomies is evidenced by improved semantic comparison of text due to successful sense disambiguation resulting in higher retrieval accuracy. Our results show significant improvements over standard text comparison alternatives.
KeywordsLatent Dirichlet Allocation Word Sense Disambiguation Inverse Pattern Ontology Match Snippet Text
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- 1.Banerjee, S., Pedersen, T.: An adapted lesk algorithm for word sense disambiguation using word-net. In: Proceedings of the Third International Conference on Intelligent Text Processing and Computational Linguistics, pp. 136–145 (2002)Google Scholar
- 7.Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In: Proceedings of The 20th International Joint Conference for Artificial Intelligence, Hyderabad, India (2007)Google Scholar
- 11.Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. In: Proc. of SIGDOC 1986: 5th International Conference on Systems Documentation, pp. 24–26 (1986)Google Scholar
- 12.Marta Sabou, M.D., Motta, E.: Exploring the semantic web as background knowledge for ontology matching, 156–190 (2008)Google Scholar
- 13.Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet: similarity - measuring the relatedness of concepts. In: Proceedings of the Nineteenth National Conference on Artificial Intelligence, AAAI 2004 (2004)Google Scholar
- 16.Recio-García, J.A., Díaz-Agudo, B., González-Calero, P.A., Sánchez-Ruiz-Granados, A.: Ontology based CBR with jcolibri. In: Applications and Innovations in Intelligent Systems XIV. SGAI 2006, pp. 149–162. Springer, Heidelberg (2006)Google Scholar
- 20.Strube, M., Ponzetto, S.P.: Wikirelate! computing semantic relatedness using wikipedia. In: AAAI 2006: Proceedings of the 21st National Conference on Artificial Intelligence, pp. 1419–1424. AAAI Press, Menlo Park (2006)Google Scholar
- 25.Zornitsa Kozareva, E.R., Hovy, E.: Semantic class learning from the web with hyponym pattern linkage graphs. In: Proceedings of ACL 2008: HLT, pp. 1048–1056. Association for Computational Linguistics, Columbus (2008)Google Scholar