Abstract
It is well known how the use of additional knowledge, coded through ontologies, can improve the quality of the results obtained, in terms of user satisfaction, when seeking information on the web. The choice of a knowledge base, as long as it is reduced to small domains, is still manageable in a semi-automatic mode. However, in wider contexts, where a higher scalability is required, a fully automatic procedure is needed.
In this paper, we show how a procedure to extract an ontology from a collection of documents can be completely automatised by making use of an optimization procedure. To this aim, we have defined a suitable fitness function and we have employed a Random Mutation Hill-Climbing algorithm to explore the solution space in order to evolve a near-optimal solution. The experimental findings show that our method is effective.
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References
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Christopher, D., Manning, P.R., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Cimiano, P.: Ontology Learning and Population from Text: Algorithms, Evaluation and Applications. Springer, Heidelberg (2006)
Clarizia, F., Greco, L., Napoletano, P.: A new technique for identification of relevant web pages in informational queries results. In: Proceedings of the 12th International Conference on Enterprise Information Systems: Databases and Information Systems Integration, pp. 70–79 (2010)
De Falco, I., Della Cioppa, A., Maisto, D., Scafuri, U., Tarantino, E.: Extremal optimization dynamics in neutral landscapes: The royal road case. In: Artificial Evolution, pp. 1–12 (2009)
Duvivier, D., Preux, P., Talbi, E.-G.: Climbing up np-hard hills. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 574–583. Springer, Heidelberg (1996)
Forrest, S., Mitchell, M.: Relative building-block fitness and the building-block hypothesis. In: Whitley, D.L. (ed.) Foundations of Genetic Algorithms 2, pp. 109–126. Morgan Kaufmann, San Mateo (1993)
Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian data analysis. Chapman & Hall, New York (1995)
Giunchiglia, F., Marchese, M., Zaihrayeu, I.: Towards a theory of formal classification. In: Proceedings of the AAAI 2005 International Workshop Contexts and Ontologies: Theory, Practice and Applications. AAAI Press, Menlo Park (2005)
Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquis. 5, 199–220 (1993)
Guarino, N.: Formal Ontology in Information Systems. IOS Press, Cambridge (1998)
Hepp, M., de Bruijn, J.: GenTax: A generic methodology for deriving OWL and RDF-S ontologies from hierarchical classifications, thesauri, and inconsistent taxonomies. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 129–144. Springer, Heidelberg (2007)
Hjørland, B.: Concept theory. Journal of the American Society for Information Science and Technology 60, 1519–1536 (2009)
Jaynes, E.T.: Probability Theory - The Logic of Science. Cambridge Press (2003)
Maedche, A.: Ontology Learning for the Semantic Web. Kluwer Academic Publishers, Dordrecht (2002)
Magnini, B., Serafini, L., Speranza, M.: Making explicit the hidden semantics of hierarchical classifications. In: AI*IA 2003: Advances in Artificial Intelligence. LNCS, pp. 436–448. Springer, Heidelberg (2003)
Mitchell, M., Holland, J.H.: When will a genetic algorithm outperform hill climbing? In: Proceedings of the 5th International Conference on Genetic Algorithms, p. 647. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Griffiths, T.L., Steyvers, M.,, J.B.T.: Topics in semantic representation. Psychological Review 114(2), 211–244 (2007)
Uschold, M., Gruninger, M.: Ontologies and semantics for seamless connectivity. SIGMOD Rec., 58–64 (2004)
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Clarizia, F., Greco, L., Napoletano, P. (2011). An Adaptive Optimisation Method for Automatic Lightweight Ontology Extraction. In: Filipe, J., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2010. Lecture Notes in Business Information Processing, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19802-1_25
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DOI: https://doi.org/10.1007/978-3-642-19802-1_25
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