@inproceedings{GavLamRig15-ICLP-IC,
editor = {De Vos, Marina and
Thomas Eiter and
Yuliya Lierler and
Francesca Toni},
title = {An abductive Framework for {Datalog+-} Ontologies},
author = {Marco Gavanelli and Evelina Lamma and Fabrizio Riguzzi and Elena Bellodi and Riccardo Zese and Giuseppe Cota},
booktitle = {Technical Communications of the 31st Int'l.
Conference on Logic Programming (ICLP 2015)},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
issn = {1613-0073},
address = {Aachen, Germany},
copyright = {by the authors},
year = {2015},
keywords = {Probabilistic Logic Programming, Lifted Inference,
Variable Elimination, Distribution Semantics, ProbLog,
Statistical Relational Artificial Intelligence},
abstract = {
Ontologies are a fundamental component of the Semantic Web since they provide a formal and machine manipulable model of a domain.
Description Logics (DLs) are often the languages of choice for modeling ontologies. Great effort has been spent in identifying decidable or even tractable fragments of DLs. Conversely, for knowledge representation and reasoning, integration with rules and rule-based reasoning is crucial in the so-called Semantic Web stack vision.
Datalog+- is an extension of Datalog which can be used for representing lightweight ontologies, and is able to express
the DL-Lite family of ontology languages, with tractable query answering under certain language restrictions.
In this work, we show that Abductive Logic Programming (ALP) is also a suitable framework for representing Datalog+- ontologies, supporting query answering through an abductive proof procedure, and smoothly achieving the integration of ontologies and rule-based reasoning.
In particular, we consider an Abductive Logic Programming framework named
SCIFF, and derived from the IFF abductive framework, able to deal with existentially (and universally) quantified variables in rule heads, and Constraint Logic Programming constraints.
Forward and backward reasoning is naturally supported in the ALP framework. We show that the SCIFF language smoothly supports the integration of rules, expressed in a Logic Programming language, with Datalog+- ontologies, mapped into SCIFF (forward) integrity constraints.
},
keywords = {Abductive Logic Programming, Datalog+-,
Description Logics,
Semantic Web.},
number = {1433},
url = {http://ceur-ws.org/Vol-1433/tc_89.pdf}
}
@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://ml.unife.it/wp-content/uploads/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{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://ml.unife.it/wp-content/uploads/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 signi cantly 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}}
}
@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{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}
}
@inproceedings{CotRigZes18-SUM-IC,
title = {A Modular Inference System for Probabilistic Description Logics},
author = {Giuseppe Cota and Fabrizio Riguzzi and Riccardo Zese and Elena Bellodi and Evelina Lamma },
booktitle = {Scalable Uncertainty Management 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings},
year = 2018,
editor = {Davide Ciucci and Gabriella Pasi and Barbara Vantaggi},
volume = {11142},
publisher = {Springer},
address = {Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
issn = {1613-0073},
doi = {10.1007/978-3-030-00461-3_6},
venue = {Milano, Italy},
eventdate = {October 3-5, 2018},
copyright = {Springer},
pages = {78-92},
isbn-print = {978-3-030-00460-6},
isbn-online = {978-3-030-00461-3},
note = {The final publication is available at Springer via \url{http://dx.doi.org/10.1007/978-3-030-00461-3_6}},
url = {http://ml.unife.it/wp-content/uploads/Papers/CotRigZes-SUM18.pdf},
scopus = {2-s2.0-85054858704}
}
@inproceedings{LosVen20-TurboExpo-IC,
author = {Losi, Enzo and Venturini, Mauro and Manservigi, Lucrezia and Ceschini, Giuseppe Fabio and Bechini, Giovanni and Cota, Giuseppe and Riguzzi, Fabrizio},
title = {Structured Methodology for Clustering Gas Turbine Transients by means of Multi-variate Time Series},
year = {2020},
publisher = {ASME},
booktitle = {Proceedings of the ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. Virtual, Online. September 21–25, 2020.},
pages = {V009T21A005},
doi = {10.1115/GT2020-14751},
volume = {9}
}
@inproceedings{LosVen21Data-TurboExpo-IC,
title = {Data Selection and Feature Engineering for the Application of Machine Learning to the Prediction of Gas Turbine Trip},
author = {Losi, Enzo and Venturini, Mauro and Manservigi, Lucrezia and Ceschini, Giuseppe Fabio and Bechini, Giovanni and Cota, Giuseppe and Riguzzi, Fabrizio},
booktitle = {Proceedings of the ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition, June 7–11, 2021 Virtual, Online},
year = {2021},
doi = {10.1115/GT2021-58914},
volume = {8},
publisher = {ASME},
pages = {V008T20A004}
}
@inproceedings{LosVen21Trip-TurboExpo-IC,
title = {Prediction of Gas Turbine Trip: a Novel Methodology Based on Random Forest Models},
author = {Losi, Enzo and Venturini, Mauro and Manservigi, Lucrezia and Ceschini, Giuseppe Fabio and Bechini, Giovanni and Cota, Giuseppe and Riguzzi, Fabrizio},
booktitle = {Proceedings of the ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition, June 7–11, 2021 Virtual, Online},
year = {2021},
publisher = {ASME},
pages = {V008T20A005},
volume = {8},
doi = {10.1115/GT2021-58916}
}
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