Abstract
In order to overcome the limitations of deductive logic-based approaches to deriving operational knowledge from ontologies, especially when data come from distributed sources, inductive (instance-based) methods may be better suited, since they are usually efficient and noise-tolerant. In this paper we propose an inductive method for improving the instance retrieval and enriching the ontology population. By casting retrieval as a classification problem with the goal of assessing the individual class-memberships w.r.t. the query concepts, we propose an extension of the k-Nearest Neighbor algorithm for OWL ontologies based on an entropic distance measure. The procedure can classify the individuals w.r.t. the known concepts but it can also be used to retrieve individuals belonging to query concepts. Experimentally we show that the behavior of the classifier is comparable with the one of a standard reasoner. Moreover we show that new knowledge (not logically derivable) is induced. It can be suggested to the knowledge engineer for validation, during the ontology population task.
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References
Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2003)
Baader, F., Ganter, B., Sertkaya, B., Sattler, U.: Completing description logic knowledge bases using formal concept analysis. In: Veloso, M. (ed.) Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, pp. 230–235 (2007)
Borgida, A., Walsh, T.J., Hirsh, H.: Towards measuring similarity in description logics. In: Horrocks, I., Sattler, U., Wolter, F. (eds.) Working Notes of the International Description Logics Workshop, Edinburgh, UK. CEUR Workshop Proceedings, vol. 147 (2005)
d’Amato, C., Fanizzi, N., Esposito, F.: Reasoning by analogy in description logics through instance-based learning. In: Tummarello, G., Bouquet, P., Signore, O. (eds.) Proceedings of Semantic Web Applications and Perspectives, 3rd Italian Semantic Web Workshop, SWAP 2006, Pisa, Italy. CEUR Workshop Proceedings, vol. 201 (2006)
Fanizzi, N., d’Amato, C., Esposito, F.: Evolutionary conceptual clustering of semantically annotated resources. In: Proceedings of the IEEE International Conference on Semantic Computing, ICSC 2007, Irvine, CA, IEEE, Los Alamitos (2007)
Fanizzi, N., d’Amato, C., Esposito, F.: A hierarchical clustering procedure for semantically annotated resources. In: Basili, R., Pazienza, M.T. (eds.) AI*IA 2007. LNCS (LNAI), vol. 4733, pp. 266–277. Springer, Heidelberg (2007)
Fanizzi, N., d’Amato, C., Esposito, F.: Induction of optimal semi-distances for individuals based on feature sets. In: Calvanese, D., Franconi, E., Haarslev, V., Lembo, D., Motik, B., Turhan, A.-Y., Tessaris, S. (eds.) Working Notes of the 20th International Description Logics Workshop, DL 2007, Bressanone, Italy. CEUR Workshop Proceedings, vol. 250 (2007)
Haase, P., van Harmelen, F., Huang, Z., Stuckenschmidt, H., Sure, Y.: A framework for handling inconsistency in changing ontologies. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 353–367. Springer, Heidelberg (2005)
Hitzler, P., Vrandec̆ić, D.: Resolution-based approximate reasoning for OWL DL. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 383–397. Springer, Heidelberg (2005)
Horrocks, I.R., Li, L., Turi, D., Bechhofer, S.K.: The Instance Store: DL reasoning with large numbers of individuals. In: Haarslev, V., Möller, R. (eds.) Proceedings of the 2004 Description Logic Workshop, DL 2004. CEUR Workshop Proceedings, vol. 104, pp. 31–40 (2004)
Möller, R., Haarslev, V., Wessel, M.: On the scalability of description logic instance retrieval. In: Parsia, B., Sattler, U., Toman, D. (eds.) Description Logics. CEUR Workshop Proceedings, vol. 189 (2006)
Sebag, M.: Distance induction in first order logic. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 264–272. Springer, Heidelberg (1997)
Wache, H., Groot, P., Stuckenschmidt, H.: Scalable instance retrieval for the semantic web by approximation. In: Dean, M., Guo, Y., Jun, W., Kaschek, R., Krishnaswamy, S., Pan, Z., Sheng, Q.Z. (eds.) WISE-WS 2005. LNCS, vol. 3807, pp. 245–254. Springer, Heidelberg (2005)
Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search – The Metric Space Approach. In: Advances in database Systems, Springer, Heidelberg (2007)
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d’Amato, C., Fanizzi, N., Esposito, F. (2008). Query Answering and Ontology Population: An Inductive Approach. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds) The Semantic Web: Research and Applications. ESWC 2008. Lecture Notes in Computer Science, vol 5021. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68234-9_23
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DOI: https://doi.org/10.1007/978-3-540-68234-9_23
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