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@article{ZesBelRig16-AMAI-IJ, author = {Riccardo Zese and Elena Bellodi and Fabrizio Riguzzi and Giuseppe Cota and Evelina Lamma }, title = {Tableau Reasoning for Description Logics and its Extension to Probabilities}, journal = {Annals of Mathematics and Artificial Intelligence}, publisher = {Springer}, copyright = {Springer}, year = {2016}, issn-print = {1012-2443}, issn-online = {1573-7470}, url = {http://ds.ing.unife.it/~friguzzi/Papers/ZesBelRig-AMAI16.pdf}, pdf = {http://rdcu.be/kONG}, doi = {10.1007/s10472-016-9529-3}, abstract = { The increasing popularity of the Semantic Web drove to a wide- spread adoption of Description Logics (DLs) for modeling real world domains. To help the diffusion of DLs, a large number of reasoning algorithms have been developed. Usually these algorithms are implemented in procedural languages such as Java or C++. Most of the reasoners exploit the tableau algorithm which features non-determinism, that is not easily handled by those languages. Prolog directly manages non-determinism, thus is a good candidate for dealing with the tableau's non-deterministic expansion rules. We present TRILL, for "Tableau Reasoner for descrIption Logics in pro- Log", that implements a tableau algorithm and is able to return explanations for queries and their corresponding probability, and TRILLP , for "TRILL powered by Pinpointing formulas", which is able to compute a Boolean for- mula representing the set of explanations for a query. Reasoning on real world domains also requires the capability of managing probabilistic and uncertain information. We show how TRILL and TRILLP can be used to compute the probability of queries to knowledge bases following DISPONTE semantics. Experiments comparing these with other systems show the feasibility of the approach.}, keywords = { Description Logics, Tableau, Prolog, Semantic Web}, scopus = {2-s2.0-84990986085} }

@article{RigBelLam16-SPE-IJ, author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese and Giuseppe Cota}, title = {Probabilistic Logic Programming on the Web}, journal = {Software: Practice and Experience}, publisher = {Wiley}, copyright = {Wiley}, year = {2016}, issn = {1097-024X}, url = {http://ds.ing.unife.it/~friguzzi/Papers/RigBelLam-SPE16.pdf}, abstract = { We present the web application "cplint on SWISH", that allows the user to write probabilistic logic programs and compute the probability of queries with just a web browser. The application is based on SWISH, a recently proposed web framework for logic programming. SWISH is based on various features and packages of SWI-Prolog, in particular its web server and its Pengine library, that allow to create remote Prolog engines and to pose queries to them. In order to develop the web application, we started from the PITA system which is included in cplint, a suite of programs for reasoning on Logic Programs with Annotated Disjunctions, by porting PITA to SWI-Prolog. Moreover, we modified the PITA library so that it can be executed in a multi-threading environment. Developing "cplint on SWISH" also required modification of the JavaScript SWISH code that creates and queries Pengines. "cplint on SWISH" includes a number of examples that cover a wide range of domains and provide interesting applications of Probabilistic Logic Programming (PLP). By providing a web interface to cplint we allow users to experiment with PLP without the need to install a system, a procedure which is often complex, error prone and limited mainly to the Linux platform. In this way, we aim to reach out to a wider audience and popularize PLP.}, keywords = { Logic Programming, Probabilistic Logic Programming, Distribution Semantics, Logic Programs with Annotated Disjunctions, Web Applications }, doi = {10.1002/spe.2386}, volume = {46}, number = {10}, pages = {1381-1396}, month = {October}, wos = {WOS:000383624900005}, scopus = {2-s2.0-84951829971} }

@inproceedings{AlbBelCot16-PLP-IW, title = {Probabilistic Constraint Logic Theories}, author = {Marco Alberti and Elena Bellodi and Giuseppe Cota and Evelina Lamma and Fabrizio Riguzzi and Riccardo Zese}, pages = {15--28}, url = {http://ceur-ws.org/Vol-1661/#paper-02}, pdf = {http://ceur-ws.org/Vol-1661/paper-02.pdf}, booktitle = {Proceedings of the 3nd International Workshop on Probabilistic Logic Programming ({PLP})}, year = 2016, editor = {Arjen Hommersom and Samer Abdallah}, volume = 1661, series = {CEUR Workshop Proceedings}, address = {Aachen, Germany}, issn = {1613-0073}, venue = {London, UK}, eventdate = {2016-09-03}, publisher = {Sun {SITE} Central Europe}, copyright = {by the authors}, abstract = {Probabilistic logic models are used ever more often to deal with the uncertain relations typical of the real world. However, these models usually require expensive inference procedures. Very recently the problem of identifying tractable languages has come to the fore. In this paper we consider the models used by the learning from interpretations ILP setting, namely sets of integrity constraints, and propose a probabilistic version of them. A semantics in the style of the distribution semantics is adopted, where each integrity constraint is annotated with a probability. These probabilistic constraint logic models assign a probability of being positive to interpretations. This probability can be computed in a time that is logarithmic in the number of ground instantiations of violated constraints. This formalism can be used as the target language in learning systems and for declaratively specifying the behavior of a system. In the latter case, inference corresponds to computing the probability of compliance of a system's behavior to the model. }, keywords = { Probabilistic Logic Programming, Distribution Semantics, Constraint Logic Theories}, scopus = {2-s2.0-84987763948} }

@inproceedings{RigBelZes16-ECAI-IC, year = {2016}, booktitle = {22nd European Conference on Artificial Intelligence {ECAI 2016}}, venue = {The Hague, Netherlands}, eventdate = {August 29-September 2, 2016}, editor = {Maria Fox and Gal Kaminka}, title = {Scaling Structure Learning of Probabilistic Logic Programs by MapReduce}, author = {Fabrizio Riguzzi and Elena Bellodi and Riccardo Zese and Giuseppe Cota and Evelina Lamma}, abstract = {Probabilistic Logic Programming is a promising formalism for dealing with uncertainty. Learning probabilistic logic programs has been receiving an increasing attention in Inductive Logic Programming: for instance, the system SLIPCOVER learns high quality theories in a variety of domains. However, SLIPCOVER is computationally expensive, with a running time of the order of hours. In order to apply SLIPCOVER to Big Data, we present SEMPRE, for ``Structure lEarning by MaPREduce", that scales SLIPCOVER by following a MapReduce strategy, directly implemented with the Message Passing Interface. }, keywords = {Probabilistic Logic Programming, Parameter Learning, Structure Learning, MapReduce}, series = {Frontiers in Artificial Intelligence and Applications}, volume = {285}, pages = {1602-1603}, url = {http://ebooks.iospress.nl/volumearticle/44940}, doi = {10.3233/978-1-61499-672-9-1602}, wos = {WOS:000385793700205}, scopus = {2-s2.0-85013029084}, copyright = {CC-BY-NC 4.0}, publisher = {IOS Press} }

@inproceedings{AlbCotRigZes16-AIIA-IC, booktitle = {Proceedings of the 15th Conference of the Italian Association for Artificial Intelligence ({AI*IA2016}), Genova, Italy, 28 November - 1 December 2016}, editor = {Giovanni Adorni and Stefano Cagnoni and Marco Gori and Marco Maratea}, year = {2016}, title = {Probabilistic Logical Inference On the Web}, author = {Marco Alberti and Giuseppe Cota and Fabrizio Riguzzi and Riccardo Zese}, abstract = {cplint on SWISH is a web application for probabilistic logic programming. It allows users to perform inference and learning using just a web browser, with the computation performed on the server. In this paper we report on recent advances in the system, namely the inclusion of algorithms for computing conditional probabilities with exact, rejection sampling and Metropolis-Hasting methods. Moreover, the system now allows hybrid programs, i.e., programs where some of the random variables are continuous. To perform inference on such programs likelihood weighting is used that makes it possible to also have evidence on continuous variables. cplint on SWISH offers also the possibility of sampling arguments of goals, a kind of inference rarely considered but useful especially when the arguments are continuous variables. Finally, cplint on SWISH offers the possibility of graphing the results, for example by drawing the distribution of the sampled continuous arguments of goals.}, publisher = {Springer International Publishing}, address = {Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, volume = {10037}, copyright = {Springer International Publishing AG}, keywords = {Probabilistic Logic Programming, Probabilistic Logical Inference, Hybrid program}, pdf = {http://ds.ing.unife.it/~friguzzi/Papers/AlbCotRig-AIXIA16.pdf}, doi = {10.1007/978-3-319-49130-1_26}, pages = {351-363}, venue = {Genova, Italy}, eventdate = {November 28-December 1, 2016}, isbn-online = {978-3-319-49129-5}, isbn-print = {978-3-319-49130-1}, issn = {0302-9743}, scopus = {2-s2.0-85006074125}, wos = {WOS:000389797400026}, note = {The final publication is available at Springer via \url{http://dx.doi.org/10.1007/978-3-319-49130-1_26}} }

@inproceedings{AlbLamRigZes16-AIIA-IC, booktitle = {Proceedings of the 15th Conference of the Italian Association for Artificial Intelligence ({AI*IA2016}), Genova, Italy, 28 November - 1 December 2016}, editor = {Giovanni Adorni and Stefano Cagnoni and Marco Gori and Marco Maratea}, year = {2016}, title = {Probabilistic Hybrid Knowledge Bases under the Distribution Semantics}, author = {Marco Alberti and Evelina Lamma and Fabrizio Riguzzi and Riccardo Zese}, abstract = {Since Logic Programming (LP) and Description Logics (DLs) are based on different assumptions (the closed and the open world assumption, respectively), combining them provides higher expressiveness in applications that require both assumptions. Several proposals have been made to combine LP and DLs. An especially successful line of research is the one based on the Lifschitz's logic of Minimal Knowledge with Negation as Failure (MKNF). Motik and Rosati introduced Hybrid knowledge bases (KBs), composed of LP rules and DL axioms, gave them an MKNF semantics and studied their complexity. Knorr et al. proposed a well-founded semantics for Hybrid KBs where the LP clause heads are non-disjunctive, which keeps querying polynomial (provided the underlying DL is polynomial) even when the LP portion is non-stratified. In this paper, we propose Probabilistic Hybrid Knowledge Bases (PHKBs), where the atom in the head of LP clauses and each DL axiom is annotated with a probability value. PHKBs are given a distribution semantics by defining a probability distribution over deterministic Hybrid KBs. The probability of a query being true is the sum of the probabilities of the deterministic KBs that entail the query. Both epistemic and statistical probability can be addressed, thanks to the integration of probabilistic LP and DLs.}, publisher = {Springer International Publishing}, address = {Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, volume = {10037}, copyright = {Springer International Publishing AG}, issn = {0302-9743}, keywords = {Probabilistic Logic Programming, Probabilistic Description Logics, Hybrid Knowledge Bases}, pdf = {http://ds.ing.unife.it/~friguzzi/Papers/AlbLamRig-AIXIA16.pdf}, doi = {10.1007/978-3-319-49130-1_27}, pages = {364-376}, venue = {Genova, Italy}, scopus = {2-s2.0-85005950065}, wos = {WOS:000389797400027}, eventdate = {November 28-December 1, 2016}, isbn-online = {978-3-319-49129-5}, isbn-print = {978-3-319-49130-1}, note = {The final publication is available at Springer via \url{http://dx.doi.org/10.1007/978-3-319-49130-1_27}} }

@inproceedings{CotZesBel16-ILP-IC, booktitle = {Inductive Logic Programming: 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers}, editor = {Katsumi Inoue and Hayato Ohwada and Akihiro Yamamoto}, title = {Distributed Parameter Learning for Probabilistic Ontologies}, author = {Giuseppe Cota and Riccardo Zese and Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma}, pdf = {http://ds.ing.unife.it/~friguzzi/Papers/CotZesBel-ILP15.pdf}, year = {2016}, publisher = {Springer International Publishing}, address = {Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, volume = {9575}, copyright = {Springer International Publishing Switzerland}, venue = {Kyoto, Japan}, eventdate = {August 20-22, 2015}, pages = {30--45}, isbn-online = {978-3-319-40566-7}, isbn-print = {978-3-319-40565-0}, issn = {0302-9743}, doi = {10.1007/978-3-319-40566-7_3}, abstract = {Representing uncertainty in Description Logics has recently received an increasing attention because of its potential to model real world domains. EDGE for Em over bDds for description loGics param- Eter learning is an algorithm for learning the parameters of probabilistic ontologies from data. However, the computational cost of this algorithm is significant since it may take hours to complete an execution. In this paper we present EDGEMR, a distributed version of EDGE that exploits the MapReduce strategy by means of the Message Passing Interface. Ex- periments on various domains show that EDGEMR signicantly reduces EDGE running time.}, keywords = {Probabilistic Description Logics, Parameter Learning, MapReduce, Message Passing Interface}, note = {The final publication is available at Springer via \url{http://dx.doi.org/10.1007/978-3-319-40566-7_3}} }

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