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@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} }

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@article{RigBelLamZes15-SW-IJ, author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese}, title = {Probabilistic Description Logics under the Distribution Semantics}, journal = {Semantic Web - Interoperability, Usability, Applicability}, volume = {6}, number = {5}, pages = {447-501}, pdf = {http://ds.ing.unife.it/~friguzzi/Papers/RigBelLamZes-SW14.pdf}, year = {2015}, doi = {10.3233/SW-140154}, abstract = { Representing uncertain information is crucial for modeling real world domains. In this paper we present a technique for the integration of probabilistic information in Description Logics (DLs) that is based on the distribution semantics for probabilistic logic programs. In the resulting approach, that we called DISPONTE, the axioms of a probabilistic knowledge base (KB) can be annotated with a real number between 0 and 1. A probabilistic knowledge base then defines a probability distribution over regular KBs called worlds and the probability of a given query can be obtained from the joint distribution of the worlds and the query by marginalization. We present the algorithm BUNDLE for computing the probability of queries from DISPONTE KBs. The algorithm 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 experimentation of BUNDLE shows that it can handle probabilistic KBs of realistic size. }, keywords = { Probabilistic Ontologies, Probabilistic Description Logics, OWL, Probabilistic Logic Programming, Distribution Semantics} }

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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} }

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@article{RigCotBel17-IJAR-IJ, author = {Fabrizio Riguzzi and Giuseppe Cota and Elena Bellodi and Riccardo Zese }, title = {Causal Inference in {cplint}}, journal = {International Journal of Approximate Reasoning}, year = {2017}, publisher = {Elsevier}, address = {Amsterdam}, copyright = {Elsevier}, doi = {10.1016/j.ijar.2017.09.007}, pdf = {http://ds.ing.unife.it/~friguzzi/Papers/RigCotBel-IJAR17.pdf}, url = {https://authors.elsevier.com/a/1VqDB,KD6ZG8M8}, abstract = { cplint is a suite of programs for reasoning and learning with Probabilistic Logic Programming languages that follow the distribution semantics. In this paper we describe how we have extended cplint to perform causal reasoning. In particular, we consider Pearl's do calculus for models where all the variables are measured. The two cplint modules for inference, PITA and MCINTYRE, have been extended for computing the effect of actions/interventions on these models. We also executed experiments comparing exact and approximate inference with conditional and causal queries, showing that causal inference is often cheaper than conditional inference. }, keywords = { Probabilistic Logic Programming, Distribution Semantics, Logic Programs with Annotated Disjunctions, ProbLog, Causal Inference, Statistical Relational Artificial Intelligence }, volume = {91}, pages = {216-232}, month = {December}, number = {Supplement C}, issn = {0888-613X} }

@article{AlbBelCot17-IA-IJ, author = {Marco Alberti and Elena Bellodi and Giuseppe Cota and Fabrizio Riguzzi and Riccardo Zese}, title = {\texttt{cplint} on {SWISH}: Probabilistic Logical Inference with a Web Browser}, journal = {Intelligenza Artificiale}, publisher = {IOS Press}, copyright = {IOS Press}, year = {2017}, issn-print = {1724-8035}, issn-online = {2211-0097}, url = {http://ds.ing.unife.it/~friguzzi/Papers/AlbBelCot-IA17.pdf}, abstract = { \texttt{cplint} on SWISH is a web application that allows users to perform reasoning tasks on probabilistic logic programs. Both inference and learning systems can be performed: 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 and particle filtering are used. \texttt{cplint} on SWISH is also able to sample goals' arguments and to graph the results. This paper reports on advances and new features of \texttt{cplint} on SWISH, including the capability of drawing the binary decision diagrams created during the inference processes. }, keywords = { Logic Programming, Probabilistic Logic Programming, Distribution Semantics, Logic Programs with Annotated Disjunctions, Web Applications }, volume = {11}, number = {1}, doi = {10.3233/IA-170106}, pages = {47--64}, wos = {WOS:000399736500004} }

@article{BelLamRig17-SPE-IJ, author = {Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi and Riccardo Zese and Giuseppe Cota}, title = {A web system for reasoning with probabilistic {OWL}}, journal = {Software: Practice and Experience}, publisher = {Wiley}, copyright = {Wiley}, year = {2017}, doi = {10.1002/spe.2410}, issn = {1097-024X}, month = {January}, pages = {125--142}, volume = {47}, number = {1}, scopus = {2-s2.0-84992412060}, url = {http://ds.ing.unife.it/~friguzzi/Papers/BelLamRig-SPE16.pdf}, abstract = { We present the web application TRILL on SWISH, which allows the user to write probabilistic Description Logic (DL) theories and compute the probability of queries with just a web browser. Various probabilistic extensions of DLs have been proposed in the recent past, since uncertainty is a fundamental component of the Semantic Web. We consider probabilistic DL theories following our DISPONTE semantics. Axioms of a DISPONTE Knowledge Base (KB) can be annotated with a probability and the probability of queries can be computed with inference algorithms. TRILL is a probabilistic reasoner for DISPONTE KBs that is implemented in Prolog and exploits its backtracking facilities for handling the non-determinism of the tableau algorithm. TRILL on SWISH is based on SWISH, a recently proposed web framework for logic programming, based on various features and packages of SWI-Prolog (e.g., a web server and a library for creating remote Prolog engines and posing queries to them). TRILL on SWISH also allows users to cooperate in writing a probabilistic DL theory. It is free, open, and accessible on the Web at the url: \trillurl; it includes a number of examples that cover a wide range of domains and provide interesting Probabilistic Semantic Web applications. By building a web-based system, we allow users to experiment with Probabilistic DLs without the need to install a complex software stack. In this way we aim to reach out to a wider audience and popularize the Probabilistic Semantic Web. }, keywords = { Semantic Web, Web Applications, Description Logics, Probabilistic Description Logics, SWI-Prolog, Logic Programming } }

@article{RigBelZes17-IJAR-IJ, author = {Fabrizio Riguzzi and Elena Bellodi and Riccardo Zese and Giuseppe Cota and Evelina Lamma }, title = {A Survey of Lifted Inference Approaches for Probabilistic Logic Programming under the Distribution Semantics}, journal = {International Journal of Approximate Reasoning}, year = {2017}, publisher = {Elsevier}, address = {Amsterdam}, copyright = {Elsevier}, doi = {10.1016/j.ijar.2016.10.002}, pdf = {http://authors.elsevier.com/a/1Tw7F,KD6ZCKEe}, url = {http://ds.ing.unife.it/~friguzzi/Papers/RigBelZes-IJAR17.pdf}, volume = {80}, number = {Supplement C}, issn = {0888-613X}, pages = {313--333}, month = {January}, abstract = { Lifted inference aims at answering queries from statistical relational models by reasoning on populations of individuals as a whole instead of considering each individual singularly. Since the initial proposal by David Poole in 2003, many lifted inference techniques have appeared, by lifting different algorithms or using approximation involving different kinds of models, including parfactor graphs and Markov Logic Networks. Very recently lifted inference was applied to Probabilistic Logic Programming (PLP) under the distribution semantics, with proposals such as LP2 and Weighted First-Order Model Counting (WFOMC). Moreover, techniques for dealing with aggregation parfactors can be directly applied to PLP. In this paper we survey these approaches and present an experimental comparison on five models. The results show that WFOMC outperforms the other approaches, being able to exploit more symmetries. }, keywords = {Probabilistic Logic Programming, Lifted Inference, Variable Elimination, Distribution Semantics, ProbLog, Statistical Relational Artificial Intelligence }, scopus = {2-s2.0-84992199737}, wos = {WOS:000391080100020} }

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@article{GavLam18-FI-IJ, author = {Gavanelli, Marco and Lamma, Evelina and Riguzzi, Fabrizio and Bellodi, Elena and Zese, Riccardo and Cota, Giuseppe}, title = {Reasoning on Datalog+- Ontologies with Abductive Logic Programming}, year = {2018}, journal = {Fundamenta Informaticae}, volume = {159}, doi = {10.3233/FI-2018-1658}, pages = {65--93}, pdf = {http://ds.ing.unife.it/~friguzzi/Papers/GavLam-FI18.pdf} }

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