Abstract
Recently, the problem of representing uncertainty in Description Logics (DLs) has received an increasing attention. In probabilistic DLs, axioms contain numeric parameters that are often difficult to specify or to tune for a human. In this paper we present an approach for learning and tuning the parameters of probabilistic ontologies from data. The resulting algorithm, called EDGE, is targeted to DLs following the DISPONTE approach, that applies the distribution semantics to DLs.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann (1994)
Bellodi, E., Riguzzi, F.: Experimentation of an expectation maximization algorithm for probabilistic logic programs. Intelligenza Artificiale 8(1), 3–18 (2012)
Bellodi, E., Riguzzi, F.: Expectation Maximization over binary decision diagrams for probabilistic logic programs. Intel. Data Anal. 17(2), 343–363 (2013)
Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: International Conference on Machine Learning, pp. 233–240. ACM (2006)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statistical Society. Series B 39(1), 1–38 (1977)
Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)
Fleischhacker, D., Völker, J.: Inductive learning of disjointness axioms. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2011, Part II. LNCS, vol. 7045, pp. 680–697. Springer, Heidelberg (2011)
Ochoa-Luna, J.E., Revoredo, K., Cozman, F.G.: Learning probabilistic description logics: A framework and algorithms. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011, Part I. LNCS, vol. 7094, pp. 28–39. Springer, Heidelberg (2011)
Riguzzi, F., Bellodi, E., Lamma, E., Zese, R.: Epistemic and statistical probabilistic ontologies. In: Uncertainty Reasoning for the Semantic Web. CEUR Workshop Proceedings, vol. 900, pp. 3–14. Sun SITE Central Europe (2012)
Riguzzi, F., Bellodi, E., Lamma, E., Zese, R.: BUNDLE: A reasoner for probabilistic ontologies. In: Faber, W., Lembo, D. (eds.) RR 2013. LNCS, vol. 7994, pp. 183–197. Springer, Heidelberg (2013)
Sato, T.: A statistical learning method for logic programs with distribution semantics. In: International Conference on Logic Programming, pp. 715–729 (1995)
Schmidt-Schauß, M., Smolka, G.: Attributive concept descriptions with complements. Artificial Intelligence 48(1), 1–26 (1991)
Völker, J., Niepert, M.: Statistical schema induction. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part I. LNCS, vol. 6643, pp. 124–138. Springer, Heidelberg (2011)
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Riguzzi, F., Bellodi, E., Lamma, E., Zese, R. (2013). Parameter Learning for Probabilistic Ontologies. In: Faber, W., Lembo, D. (eds) Web Reasoning and Rule Systems. RR 2013. Lecture Notes in Computer Science, vol 7994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39666-3_26
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DOI: https://doi.org/10.1007/978-3-642-39666-3_26
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