@inproceedings{RigBelLamZes13-AIIA13-IC, title = {Computing Instantiated Explanations {in~OWL~DL}}, author = { Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese}, booktitle = {Proceedings of the 13th Conference of the Italian Association for Artificial Intelligence ({AI*IA2013}), Turin, Italy, 4-6 December 2013}, editor = {Matteo Baldoni and Cristina Baroglio and Guido Boella}, year = {2013}, pages = {397-408}, volume = {8249}, publisher = {Springer}, copyright = {Springer}, series = {Lecture Notes in Artificial Intelligence}, address = {Heidelberg, Germany}, pdf = {http://ml.unife.it/wp-content/uploads/Papers/RigBelLamZes-AIIA13.pdf}, note = {The original publication is available at \url{http://link.springer.com}}, doi = {10.1007/978-3-319-03524-6_34} }

@inproceedings{ZesBelLamRig13-CILC13-NC, title = {A Description Logics Tableau Reasoner in {Prolog}}, author = {Riccardo Zese and Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi}, booktitle = {Proceedings of the 28th Italian Conference on Computational Logic ({CILC2013}), Catania, Italy, 25-27 September 2013}, editor = {Domenico Cantone and Marianna Nicolosi Asmundo}, year = {2013}, series = {CEUR Workshop Proceedings}, publisher = {Sun {SITE} Central Europe}, issn = {1613-0073}, number = {1068}, address = {Aachen, Germany}, pages = {33-47}, pdf = {http://ml.unife.it/wp-content/uploads/Papers/ZesBelLamRig-CILC13.pdf}, url = {http://ceur-ws.org/Vol-1068/paper-l02.pdf}, copyright = {by the authors} }

@inproceedings{RigBelLamZese13-RR13b-IC, title = {{BUNDLE}: A Reasoner for Probabilistic Ontologies}, author = {Fabrizio Riguzzi and Evelina Lamma and Elena Bellodi and Riccardo Zese}, booktitle = {7th International Conference on Web Reasoning and Rule Systems (RR 2013), Mannheim, Germany, July 27-29 2013. Proceedings}, editor = {Faber, Wolfgang and Lembo, Domenico}, year = {2013}, volume = {7994}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, address = {Heidelberg, Germany}, isbn = {978-3-642-39665-6}, copyright = {Springer}, pages = {183-197}, doi = {10.1007/978-3-642-39666-3_14}, pdf = {http://ml.unife.it/wp-content/uploads/Papers/RigBelLam-RR13b.pdf}, abstract = { Representing uncertain information is very important for modeling real world domains. Recently, the DISPONTE semantics has been proposed for probabilistic description logics. In DISPONTE, the axioms of a knowledge base can be annotated with a set of variables and a real number between 0 and 1. This real number represents the probability of each version of the axiom in which the specified variables are instantiated. In this paper we present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases that follow the $\mathcal{ALC}$ semantics. BUNDLE exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed. The experiments performed by applying BUNDLE to probabilistic knowledge bases show that it can handle ontologies of realistic size and is competitive with the system PRONTO for the probabilistic description logic P-$\mathcal{SHIQ}$(D). }, keywords = {Probabilistic Ontologies, Probabilistic Description Logics, OWL, Probabilistic Logic Programming, Distribution Semantics}, note = {The original publication is available at \url{http://link.springer.com}} }

@inproceedings{RigBelLamZese13-RR13a-IC, author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese}, title = {Parameter Learning for Probabilistic Ontologies}, booktitle = {7th International Conference on Web Reasoning and Rule Systems (RR 2013), Mannheim, Germany, July 27-29 2013. Proceedings}, editor = {Faber, Wolfgang and Lembo, Domenico}, year = {2013}, volume = {7994}, isbn = {978-3-642-39665-6}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, address = {Heidelberg, Germany}, copyright = {Springer}, pages = {265-270}, doi = {10.1007/978-3-642-39666-3_26}, pdf = {http://ml.unife.it/wp-content/uploads/Papers/RigBelLam-RR13a.pdf}, note = {The original publication is available at \url{http://link.springer.com}}, 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, for Em over bDds for description loGics paramEter learning, is targeted to DLs following the DISPONTE approach, that applies the distribution semantics to DLs.}, keywords = {Statistical Relational Learning, Probabilistic Inductive Logic Programming, Probabilistic Logic Programming, Expectation Maximization, Binary Decision Diagrams, Logic Programs with Annotated Disjunctions} }

@article{BelRig13-IDA-IJ, author = {Elena Bellodi and Fabrizio Riguzzi}, title = { Expectation {Maximization} over Binary Decision Diagrams for Probabilistic Logic Programs}, year = {2013}, volume = {17}, number = {2}, journal = {Intelligent Data Analysis}, publisher = {IOS Press}, copyright = {IOS Press}, pages = {343-363}, doi = {10.3233/IDA-130582}, pdf = {http://ml.unife.it/wp-content/uploads/Papers/BelRig13-IDA-IJ.pdf}, abstract = {Recently much work in Machine Learning has concentrated on using expressive representation languages that combine aspects of logic and probability. A whole field has emerged, called Statistical Relational Learning, rich of successful applications in a variety of domains. In this paper we present a Machine Learning technique targeted to Probabilistic Logic Programs, a family of formalisms where uncertainty is represented using Logic Programming tools. Among various proposals for Probabilistic Logic Programming, the one based on the distribution semantics is gaining popularity and is the basis for languages such as ICL, PRISM, ProbLog and Logic Programs with Annotated Disjunctions. This paper proposes a technique for learning parameters of these languages. Since their equivalent Bayesian networks contain hidden variables, an Expectation Maximization (EM) algorithm is adopted. In order to speed the computation up, expectations are computed directly on the Binary Decision Diagrams that are built for inference. The resulting system, called EMBLEM for ``EM over Bdds for probabilistic Logic programs Efficient Mining'', has been applied to a number of datasets and showed good performances both in terms of speed and memory usage. In particular its speed allows the execution of a high number of restarts, resulting in good quality of the solutions.}, keywords = {Statistical Relational Learning, Probabilistic Inductive Logic Programming, Probabilistic Logic Programs, Logic Programs with Annotated Disjunctions, Expectation Maximization, Binary Decision Diagrams } }

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