@incollection{RigBel14-URSWa-BC, year = {2014}, isbn = {978-3-319-13412-3}, booktitle = {Uncertainty Reasoning for the Semantic Web III}, series = {Lecture Notes in Computer Science}, editor = {Bobillo, Fernando and Carvalho, Rommel N. and Costa, Paulo C.G. and d'Amato, Claudia and Fanizzi, Nicola and Laskey, Kathryn B. and Laskey, Kenneth J. and Lukasiewicz, Thomas and Nickles, Matthias and Pool, Michael}, doi = {10.1007/978-3-319-13413-0_4}, title = {Learning Probabilistic Description Logics}, publisher = {Springer International Publishing}, copyright = {Springer International Publishing}, author = {Riguzzi, Fabrizio and Bellodi, Elena and Lamma, Evelina and Zese, Riccardo and Cota, Giuseppe}, pages = {63-78}, pdf = {http://ml.unife.it/wp-content/uploads/Papers/RigBel14-URSWa-BC.pdf}, language = {English}, volume = {8816}, note = {The original publication is available at \url{http://link.springer.com}} }

@incollection{RigBel14-URSWb-BC, year = {2014}, isbn = {978-3-319-13412-3}, booktitle = {Uncertainty Reasoning for the Semantic Web III}, series = {Lecture Notes in Computer Science}, editor = {Bobillo, Fernando and Carvalho, Rommel N. and Costa, Paulo C.G. and d'Amato, Claudia and Fanizzi, Nicola and Laskey, Kathryn B. and Laskey, Kenneth J. and Lukasiewicz, Thomas and Nickles, Matthias and Pool, Michael}, doi = {10.1007/978-3-319-13413-0_5}, title = {Semantics and Inference for Probabilistic Description Logics}, publisher = {Springer International Publishing}, copyright = {Springer International Publishing}, author = {Zese, Riccardo and Bellodi, Elena and Lamma, Evelina and Riguzzi, Fabrizio and Aguiari, Fabiano}, pages = {79-99}, language = {English}, volume = {8816}, url = {http://ml.unife.it/wp-content/uploads/Papers/RigBel14-URSWb-BC.pdf}, note = {The original publication is available at \url{http://link.springer.com}} }

@article{RigBelZes14-FAI-IJ, author = {Riguzzi, Fabrizio and Bellodi, Elena and Zese, Riccardo}, title = {A History of Probabilistic Inductive Logic Programming}, journal = {Frontiers in Robotics and AI}, volume = {1}, year = {2014}, number = {6}, url = {http://www.frontiersin.org/computational_intelligence/10.3389/frobt.2014.00006/abstract}, doi = {10.3389/frobt.2014.00006}, issn = {2296-9144}, abstract = {The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20?years, with many proposals for languages that combine probability with logic programming. Since the start, the problem of learning probabilistic logic programs has been the focus of much attention. Learning these programs represents a whole subfield of Inductive Logic Programming (ILP). In Probabilistic ILP (PILP), two problems are considered: learning the parameters of a program given the structure (the rules) and learning both the structure and the parameters. Usually, structure learning systems use parameter learning as a subroutine. In this article, we present an overview of PILP and discuss the main results.}, pages = {1-5}, keywords = {logic programming, probabilistic programming, inductive logic programming, probabilistic logic programming, statistical relational learning}, copyright = {by the authors} }

@article{BelLamRig14-ICLP-IJ, author = { Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi and Santos Costa, Vitor and Riccardo Zese}, title = {Lifted Variable Elimination for Probabilistic Logic Programming}, journal = {Theory and Practice of Logic Programming}, publisher = {Cambridge University Press}, copyright = {Cambridge University Press}, number = {Special issue 4-5 - ICLP 2014}, volume = {14}, year = {2014}, pages = {681-695}, doi = {10.1017/S1471068414000283}, pdf = {http://arxiv.org/abs/1405.3218}, keywords = {Probabilistic Logic Programming, Lifted Inference, Variable Elimination, Distribution Semantics, ProbLog, Statistical Relational Artificial Intelligence}, abstract = {Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. These factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. The resulting algorithm, called LP2 for Lifted Probabilistic Logic Programming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.}, isi = {000343203200019}, scopus = {84904624147} }

@inproceedings{RigBelLamZes14-ILP13-IC, author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese}, title = {Learning the Parameters of Probabilistic Description Logics}, booktitle = { Late Breaking papers of the 23rd International Conference on Inductive Logic Programming, Rio de Janeiro, Brazil, August 28th to 30th, 2013}, editor = {Gerson Zaverucha and Santos Costa, Vitor and Aline Marins Paes}, year = {2014}, volume = {1187}, series = {CEUR Workshop Proceedings}, publisher = {Sun {SITE} Central Europe}, address = {Aachen, Germany}, issn = {1613-0073}, url = {http://ceur-ws.org/Vol-1187/paper-08.pdf}, pages = {46-51}, copyright = {by the authors} }

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