@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://ml.unife.it/wp-content/uploads/Papers/RigCotBel-IJAR17.pdf},
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},
scopus = {2-s2.0-84992199737},
wos = {WOS:000391080100020}
}
@incollection{RigZesCot17-EKAW-IC,
title = {Probabilistic Inductive Logic Programming on the Web},
author = {Fabrizio Riguzzi and Riccardo Zese and Giuseppe Cota},
booktitle = {20th International Conference on Knowledge Engineering and Knowledge Management,
{EKAW} 2016; Bologna; Italy; 19 November 2016 through 23 November 2016},
year = {2017},
publisher = {Springer},
address = {Cham},
series = {Lecture Notes in Computer Science},
volume = {10180},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/RigZesCot-EKAW16.pdf},
isbn-online = {978-3-319-58694-6},
isbn-print = {978-3-319-58693-9},
pages = {172--175},
scopus = {2-s2.0-85019730114},
doi = {10.1007/978-3-319-58694-6_25},
abstract = {Probabilistic Inductive Logic Programming (PILP) is gaining attention for its
capability of modeling complex domains containing uncertain relationships among entities.
Among PILP systems, \texttt{cplint} provides inference and learning algorithms competitive with the state of the art. Besides parameter
learning, \texttt{cplint} provides one of the few structure learning
algorithms for PLP, SLIPCOVER.
Moreover, an online version was recently developed, \texttt{cplint} on SWISH, that allows users to experiment with the system using just a
web browser.
In this demo we illustrate \texttt{cplint} on SWISH concentrating
on structure learning with SLIPCOVER.
\texttt{cplint} on SWISH also includes many examples and a step-by-step tutorial.},
keywords = {Probabilistic Inductive Logic Programming, Probabilistic Logic Programming,
Inductive Logic Programming},
copyright = {Springer International Publishing AG},
note = {The final publication is available at Springer via
\url{http://dx.doi.org/10.1007/978-3-319-58694-6_25}},
venue = {Bologna, Italy},
eventdate = {November 19-November 23, 2016}
}
@inproceedings{RigLamAlb17-URANIA-IW,
title = {Probabilistic Logic Programming for Natural Language Processing },
author = {Fabrizio Riguzzi and Evelina Lamma and Marco Alberti and Elena Bellodi and Riccardo Zese and Giuseppe Cota},
pages = {30--37},
url = {http://ceur-ws.org/Vol-1802/},
pdf = {http://ceur-ws.org/Vol-1802/paper4.pdf},
booktitle = {{URANIA} 2016,
Deep Understanding and Reasoning: A Challenge for Next-generation Intelligent Agents,
Proceedings of the {AI*IA} Workshop on Deep Understanding and Reasoning: A Challenge for Next-generation Intelligent Agents 2016
co-located with 15th International Conference of the Italian Association for Artificial Intelligence ({AIxIA} 2016)},
year = 2017,
editor = {Federico Chesani and Paola Mello and Michela Milano},
volume = 1802,
series = {CEUR Workshop Proceedings},
address = {Aachen, Germany},
issn = {1613-0073},
venue = {Genova, Italy},
eventdate = {2016-11-28},
publisher = {Sun {SITE} Central Europe},
copyright = {by the authors},
abstract = {The ambition of Artificial Intelligence is to solve problems without human intervention. Often the problem description is given in human (natural) language. Therefore it is crucial to find an automatic way to understand a text written by a human. The research field concerned with the interactions between computers and natural languages is known under the name of Natural Language Processing (NLP), one of the most studied fields of Artificial Intelligence.
In this paper we show that Probabilistic Logic Programming (PLP) is a suitable approach for NLP in various scenarios. For this purpose we use \texttt{cplint} on SWISH, a web application for Probabilistic Logic Programming. \texttt{cplint} on SWISH allows users to perform inference and learning with the framework \texttt{cplint} using just a web browser, with the computation performed on the server.},
keywords = {Probabilistic Logic Programming, Probabilistic Logical Inference, Natural Language Processing},
scopus = {2-s2.0-85015943369}
}
@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://ml.unife.it/wp-content/uploads/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://ml.unife.it/wp-content/uploads/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},
url = {http://ml.unife.it/wp-content/uploads/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}
}
@inproceedings{GavLamRig17-JURISIN-IC,
author = {Gavanelli, Marco
and Lamma, Evelina
and Riguzzi, Fabrizio
and Bellodi, Elena
and Riccardo, Zese
and Cota, Giuseppe},
editor = {Otake, Mihoko
and Kurahashi, Setsuya
and Ota, Yuiko
and Satoh, Ken
and Bekki, Daisuke},
title = {Abductive Logic Programming for Normative Reasoning and Ontologies},
booktitle = {New Frontiers in Artificial Intelligence: JSAI-isAI 2015 Workshops,
LENLS, JURISIN, AAA, HAT-MASH, TSDAA, ASD-HR, and SKL, Kanagawa, Japan, November 16-18, 2015, Revised Selected Papers},
year = {2017},
publisher = {Springer International Publishing},
copyright = {Springer International Publishing AG},
series = {Lecture Notes in Computer Science},
volume = {10091},
address = {Cham},
pages = {187--203},
isbn-online = {978-3-319-50953-2},
isbn-print = {978-3-319-50952-5},
doi = {10.1007/978-3-319-50953-2_14},
scopus = {2-s2.0-85018397999}
}
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