Learning Probabilistic Description Logics

Author:

Riguzzi Fabrizio,Bellodi Elena,Lamma Evelina,Zese Riccardo,Cota Giuseppe

Publisher

Springer International Publishing

Reference29 articles.

1. 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)

2. Bellodi, E., Lamma, E., Riguzzi, F., Albani, S.: A distribution semantics for probabilistic ontologies. In: Uncertainty Reasoning for the Semantic Web. CEUR Workshop Proceedings, vol. 778, pp. 75–86. Sun SITE Central Europe (2011)

3. Bellodi, E., Riguzzi, F.: Experimentation of an expectation maximization algorithm for probabilistic logic programs. Intelligenza Artificiale 8(1), 3–18 (2012)

4. Bellodi, E., Riguzzi, F.: Expectation Maximization over binary decision diagrams for probabilistic logic programs. Intell. Data Anal. 17(2), 343–363 (2013)

5. Bellodi, E., Riguzzi, F.: Structure learning of probabilistic logic programs by searching the clause space. Theory and Practice of Logic Programming FirstView Articles (to appear, 2014)

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1. Tableau reasoning for description logics and its extension to probabilities;Annals of Mathematics and Artificial Intelligence;2016-10-10

2. Probabilistic Hybrid Knowledge Bases Under the Distribution Semantics;AI*IA 2016 Advances in Artificial Intelligence;2016

3. Distributed Parameter Learning for Probabilistic Ontologies;Inductive Logic Programming;2016

4. Learning Probabilistic Description Logics;Uncertainty Reasoning for the Semantic Web III;2014

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