Semantic Relation Extraction By Analysis Of Terms Correlation In Documents

Ontologies are important to organize and describe information, but are hard to create and maintain, which motivates the development of tools to help in this task. This article presents a strategy to extract, from a corpora of documents in a given domain, semantic elements expressing proximity relations between terms and concepts to help the construction of domain ontologies. The technique presented here, ACT, is based on linguistic processing, machine learning, and biclustering. Results show that concepts obtained by ACT are at least as good as those from similar techniques, such as LSI and NMF. In relation to those techniques, it additionally has the advantage of allowing the supervision by a domain expert. © 2009 IEEE.1726Akkaya, K., Tunc, C., Aktas, D., Altintas, A., On the number of clusters in channel model (2006) Proceedings of the Spread Spectrum Techniques and Applications, pp. 6-9. , doi: 10.1109/ISSSTA.2006.311723Cheng, Y., Church, G., Biclustering of expression data (2000) Proc. ISMB'00, pp. 93-103Dumais, S.T., Berry, M.W., Brien, G.W.O., Using linear algebra for intelligent information retrieval (1995) SIAM, pp. 573-595Fortuna, B., Grobelnik, M., Mladenic, D., (2006) System for Semi-automatic Ontology Construction, 4289, pp. 121-131. , Springer Berlin / HeidelbergGonzalez, M., Lima, V.L.S., Recuperação de informação e processamento de linguagem natural (2003) XXIII Congresso Da Sociedade Brasileira de Computação, 3, pp. 347-395Hearst, M.A., (1998) Automated Discovery of WordNet Relations, in WordNet: An Electronic Lexical Database, , MIT PressHorng, Y.-J., Chen, S.-M., Chang, Y.-C., Lee, C.-H., A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques (2005) IEEE T. Fuzzy Systems, 13 (2), pp. 216-228Lee, D.D., Seung, H.S., Learning the parts of objects by non-negative matrix factorization (1999) Nature, 401 (6755), pp. 788-791Peat, H.J., Willett, P., The limitations of term co-occurrence data for query expansion in document retrieval systems (1991) Journal of the American Society for Information Science, 42, pp. 378-383Pereira, R., Ricarte, I., Gomide, F., Information retrieval with FROM: The fuzzy relational ontological model (2009) International Journal of Intelligent Systems, 24, pp. 340-356Salton, G., Buckley, C., Term-weighting approaches in automatic text retrieval (1988) Information Processing & Management, 24, pp. 513-523Shahnaz, F., Berry, M., Pauca, P., Plemmons, R., Document clustering using nonnegative matrix factorization (2006) Journal on Information Processing and Management, pp. 373-386Velardi, P., Missikoff, M., Basili, R., Identification of relevant terms to support the construction of domain ontologies (2001) Proceedings of the Workshop on Human Language Technology and Knowledge Management, pp. 1-8. , Morristown, NJ, USA, Association for Computational Linguistics. doi: http://dx.doi.org/10.3115/1118220.111822