Abstract
This paper investigates learning methods where the target language is the recently proposed probabilistic description logic cr \(\mathcal{ALC}\). We start with an inductive logic programming algorithm that learns logical constructs; we then develop an algorithm that learns probabilistic constructs by searching for conditioning concepts, using examples given as interpretations. Issues on learning from entailments are also examined, and practical examples are discussed.
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References
Antoniou, G., van Harmelen, F.: Semantic Web Primer. MIT Press, Cambridge (2008)
Baader, F., Nutt, W.: Basic description logics. In: Description Logic Handbook, pp. 47–100. Cambridge University Press, Cambridge (2002)
Cozman, F.G., Polastro, R.B.: Loopy propagation in a probabilistic description logic. In: Greco, S., Lukasiewicz, T. (eds.) SUM 2008. LNCS (LNAI), vol. 5291, pp. 120–133. Springer, Heidelberg (2008)
Cozman, F.G., Polastro, R.B.: Complexity analysis and variational inference for interpretation-based probabilistic description logics. In: Conference on Uncertainty in Artificial Intelligence (2009)
De Raedt, L. (ed.): Advances in Inductive Logic Programming. IOS Press, Amsterdam (1996)
De Raedt, L.: Logical settings for concept-learning. Artificial Intelligence 95(1), 187–201 (1997)
De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.): Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911. Springer, Heidelberg (2008)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal Royal Statistical Society B 44, 1–38 (1977)
Fanizzi, N., D’Amato, C., Esposito, F.: DL-FOIL concept learning in description logics. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS (LNAI), vol. 5194, pp. 107–121. Springer, Heidelberg (2008)
Heinsohn, J.: Probabilistic description logics. In: International Conf. on Uncertainty in Artificial Intelligence, pp. 311–318 (1994)
Jaeger, M.: Probabilistic reasoning in terminological logics. In: Principals of Knowledge Representation (KR), pp. 461–472 (1994)
Jaeger, M.: Relational bayesian networks: a survey. Linkoping Electronic Articles in Computer and Information Science 6 (2002)
Lehmann, J.: Hybrid learning of ontology classes. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 883–898. Springer, Heidelberg (2007)
Ochoa-Luna, J.E., Cozman, F.G.: An algorithm for learning with probabilistic description logics. In: 5th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW) at the 8th International Semantic Web Conference (ISWC), Chantilly, USA, pp. 63–74 (2009)
Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)
Sebastiani, F.: A probabilistic terminological logic for modelling information retrieval. In: ACM Conf. on Research and Development in Information Retrieval (SIGIR), pp. 122–130 (1994)
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Revoredo, K., Ochoa-Luna, J.E., Cozman, F.G. (2010). Learning Terminologies in Probabilistic Description Logics. In: da Rocha Costa, A.C., Vicari, R.M., Tonidandel, F. (eds) Advances in Artificial Intelligence – SBIA 2010. SBIA 2010. Lecture Notes in Computer Science(), vol 6404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16138-4_5
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DOI: https://doi.org/10.1007/978-3-642-16138-4_5
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