@inproceedings{BelRigLam09-RICERCA-RCRA-IW,
author = {Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma},
title = {Mining Probabilistic Declarative Process Models},
booktitle = { Session {R.i.C.e.R.c.A}: RCRA Incontri E Confronti of the 16th RCRA International Workshop on Experimental evaluation of algorithms for solving
problems with combinatorial explosion ({RCRA} 2009)
Reggio Emilia, Italy, 11-12 December 2009},
editor = {Marco Gavanelli and Toni Mancini},
url = {http://ml.unife.it/wp-content/uploads/Papers/BelRigLam09-RICERCA-RCRA-IW.pdf},
year = {2009},
keywords = {Process Mining, Learning from Interpretations, Business Processes, Probabilistic Relational Languages},
abstract = {The management of business processes has recently received a lot of attention from companies, since it can support efficiency improvement. We present an approach for mining process models that first induces a model in the SCIFF logical language and then translates the model into Markov logic, a language belonging to the field of statistical relational learning.
Markov logic attaches weights to first-order contraints, in order to obtain a final probabilistic classification of process traces better than the purely logical one. The data used for learning and testing belong to a real database of university students' careers.}
}
@inproceedings{BelLamRigAlb11-URSW11-IW,
author = {Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi and Simone Albani },
editor = {Fernando Bobillo and
Rommel Carvalho and
da Costa, Paulo C. G. and
d'Amato, Claudia and
Nicola Fanizzi and
Laskey, Kathryn B. and
Laskey, Kenneth J. and
Thomas Lukasiewicz and
Trevor Martin and
Matthias Nickles and
Michael Pool},
title = {A Distribution Semantics for Probabilistic Ontologies},
booktitle = {Proceedings ot the 7th International Workshop on Uncertainty Reasoning for the Semantic Web, Bonn, Germany, 23 October, 2011 },
year = {2011},
url = {http://ml.unife.it/wp-content/uploads/Papers/BelLamRigAlb-URSW11.pdf},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
issn = {1613-0073},
address = {Aachen, \Germany},
volume = {778},
pages = {75-86},
pdf = {http://ceur-ws.org/Vol-778/paper7.pdf},
abstract = {We present DISPONTE, a semantics for probabilistic ontologies that is based on the distribution semantics for probabilistic logic programs. In DISPONTE each axiom of a probabilistic ontology is annotated with a probability. The probabilistic theory defines thus a distribution over normal theories (called worlds) obtained by including an axiom in a world with a probability given by the annotation. The probability of a query is computed from this distribution with marginalization.
We also present the system BUNDLE for reasoning over probabilistic OWL DL ontologies according to the DISPONTE semantics. BUNDLE is based on Pellet and uses its capability of returning explanations for a query. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed.}
}
@inproceedings{BelRig11-MCP11-IW,
author = {Elena Bellodi and Fabrizio Riguzzi},
title = {An {Expectation Maximization} Algorithm for Probabilistic Logic Programs},
booktitle = {Proceedings of the Workshop on Mining Complex Patterns ({MCP2011}), 17 September 2011},
address = {Palermo, Italy},
editor = {Appice, Annalisa and Ceci, Michelangelo and Loglisci, Corrado and Manco, Giuseppe},
year = {2011},
month = sep,
pages = {26-37},
abstract = {
Recently much work in Machine Learning has concentrated on representation languages able to combine aspects of logic and probability, leading to the birth of a whole field called Statistical Relational Learning.
In this paper we present a technique for parameter learning targeted to a family of formalisms where uncertainty is represented using Logic Programming tools - the so-called Probabilistic Logic Programs such as ICL, PRISM, ProbLog and LPAD.
Since their equivalent Bayesian networks contain hidden variables, an EM algorithm is adopted.
In order to speed the computation, expectations are computed directly on the Binary Decision Diagrams that are built for inference.
The resulting system, called EMBLEM for ``EM over BDDs for probabilistic Logic programs Efficient Mining'', has been applied to a number of datasets and showed good performances both in terms of speed and memory.
},
url = {http://ml.unife.it/wp-content/uploads/Papers/BelRig-MCP11.pdf},
copyright = {by the authors},
keywords = { Statistical Relational Learning, Probabilistic Logic Programs, Logic Programs with Annotated Disjunction, Expectation Maximization, Binary Decision Diagrams}
}
@inproceedings{RigBelLamZes12-URSW12-IW,
author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese},
title = {Epistemic and Statistical Probabilistic Ontologies},
booktitle = {Proceedings of the 8th International Workshop on Uncertain Reasoning for the Semantic Web (URSW2012), Boston, USA, 11 November 2012},
year = {2012},
editor = {Fernando Bobillo and
Rommel Carvalho and
da Costa, Paulo C. G. and
Nicola Fanizzi and
Laskey, Kathryn B. and
Laskey, Kenneth J. and
Thomas Lukasiewicz and
Trevor Martin and
Matthias Nickles and
Michael Pool},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
issn = {1613-0073},
address = {Aachen, Germany},
number = {900},
pages = {3-14},
pdf = {http://ceur-ws.org/Vol-900/paper1.pdf},
abstract = {We present DISPONTE, a semantics for probabilistic ontologies that is based on the distribution semantics for probabilistic logic programs. In DISPONTE the axioms of a probabilistic ontology can be
annotated with an epistemic or a statistical probability. The epistemic probability represents a degree of confidence in the axiom, while the statistical probability considers the populations to which the axiom is applied.}
}
@inproceedings{BelRig12-AIIADC12-IW,
title = {Parameter and Structure Learning Algorithms for Statistical Relational Learning},
pages = {5-9},
author = {Elena Bellodi and Fabrizio Riguzzi },
editor = {Paolo Liberatore and
Michele Lombardi and
Floriano Scioscia},
booktitle = {Doctoral Consortium of the 12th AI*IA Symposium on Artificial Intelligence, Proceedings of the Doctoral Consortium of the 12th Symposium of the Italian Association for Artificial Intelligence
Rome, Italy, June 15, 2012},
copyright = {by the authors},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
issn = {1613-0073},
address = {Aachen, Germany},
volume = {926},
year = {2012},
pdf = {http://ceur-ws.org/Vol-926/paper1.pdf},
scopus = {2-s2.0-84891770795
},
abstract = {My research activity focuses on the field of Machine Learning. Two key challenges
in most machine learning applications are uncertainty and complexity.
The standard framework for handling uncertainty is probability, for complexity
is first-order logic. Thus we would like to be able to learn and perform inference
in representation languages that combine the two. This is the focus of the field
of Statistical Relational Learning.
},
keywords = {Statistical relational learning, machine learning, first order logic}
}
@inproceedings{RigBelLam12-DL12-IW,
author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma},
title = {Probabilistic {Datalog+/-} under the Distribution Semantics},
booktitle = {Proceedings of the 25th International Workshop on Description Logics ({DL2012}),
Roma, Italy, 7-10 June 2012},
editor = {Yevgeny Kazakov and Domenico Lembo and Frank Wolter},
year = {2012},
abstract = {We apply the distribution semantics for probabilistic ontologies (named DISPONTE) to the Datalog+/- language.
In DISPONTE the formulas of a probabilistic ontology can be annotated with an epistemic or a statistical probability. The epistemic probability represents a degree of confidence in the formula, while the statistical probability considers the populations to which the formula is applied.
The probability of a query is defined in terms of finite set of finite explanations for the query, where an explanation is a set of possibly instantiated formulas that is sufficient for entailing the query.
The probability of a query is computed from the set of explanations by making them mutually exclusive.
We also compare the DISPONTE approach for Datalog+/- ontologies with that of Probabilistic Datalog+/-, where an ontology is composed of a Datalog+/- theory whose formulas are associated to an assignment of values for the random variables of a companion Markov Logic Network.
},
copyright = {by the authors},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
issn = {1613-0073},
address = {Aachen, Germany},
url = {http://ml.unife.it/wp-content/uploads/Papers/RigBelLam12-DL12.pdf},
pdf = {http://ceur-ws.org/Vol-846/paper_25.pdf},
volume = {846},
pages = {519-529}
}
@inproceedings{CotZes15-AIIADC-IW,
title = {Learning Probabilistic Ontologies with Distributed Parameter Learning },
author = {Giuseppe Cota and Riccardo Zese and Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi},
pages = {7--12},
pdf = {http://ceur-ws.org/Vol-1485/paper2.pdf},
booktitle = {Proceedings of the Doctoral Consortium (DC)
co-located with the 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015)},
year = 2015,
editor = {Elena Bellodi and Alessio Bonfietti},
volume = 1485,
series = {CEUR Workshop Proceedings},
address = {Aachen, Germany},
issn = {1613-0073},
venue = {Ferrara, Italy},
eventdate = {2015-09-23/24},
publisher = {Sun {SITE} Central Europe},
copyright = {by the authors},
abstract = {
We consider the problem of learning both the structure and
the parameters of Probabilistic Description Logics under DISPONTE.
DISPONTE ("DIstribution Semantics for Probabilistic ONTologiEs")
adapts the distribution semantics for Probabilistic Logic Programming
to Description Logics. The system LEAP for "LEArning Probabilistic
description logics" learns both the structure and the parameters of
DISPONTE knowledge bases (KBs) by exploiting the algorithms CELOE
and EDGE. The former stands for "Class Expression Learning for Ontology
Engineering" and it is used to generate good candidate axioms
to add to the KB, while the latter learns the probabilistic parameters
and evaluates the KB. EDGE for "Em over bDds for description loGics
paramEter learning" is an algorithm for learning the parameters of probabilistic
ontologies from data. In order to contain the computational cost,
a distributed version of EDGE called EDGEMR was developed. EDGEMR
exploits the MapReduce (MR) strategy by means of the Message Passing
Interface. In this paper we propose the system LEAPMR. It is a
re-engineered version of LEAP which is able to use distributed parallel
parameter learning algorithms such as EDGEMR.
},
keywords = {Probabilistic Description Logics, Structure Learning,
Parameter Learning, MapReduce, Message Passing Interface.
}
}
@inproceedings{ZesBel15-AIIADC-IW,
title = {Tableau Reasoners for Probabilistic Ontologies Exploiting Logic Programming Techniques},
author = {Riccardo Zese and Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma},
pages = {1--6},
pdf = {http://ceur-ws.org/Vol-1485/paper1.pdf},
booktitle = {Proceedings of the Doctoral Consortium (DC)
co-located with the 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015)},
year = 2015,
editor = {Elena Bellodi and Alessio Bonfietti},
volume = 1485,
series = {CEUR Workshop Proceedings},
address = {Aachen, Germany},
issn = {1613-0073},
venue = {Ferrara, Italy},
eventdate = {2015-09-23/24},
publisher = {Sun {SITE} Central Europe},
copyright = {by the authors},
abstract = {The adoption of Description Logics for modeling real world
domains within the Semantic Web is exponentially increased in the last
years, also due to the availability of a large number of reasoning algorithms.
Most of them exploit the tableau algorithm which has to manage
non-determinism, a feature that is not easy to handle using procedural
languages such as Java or C++. Reasoning on real world domains also
requires the capability of managing probabilistic and uncertain information.
We thus present TRILL, for "Tableau Reasoner for descrIption
Logics in proLog" and TRILLP
, for "TRILL powered by Pinpointing
formulas", which implement the tableau algorithm and return the probability
of queries. TRILLP
, instead of the set of explanations for a query,
computes a Boolean formula representing them, speeding up the computation.
},
keywords = {Distribution Semantics, Probabilistic Semantic Web,
Logic Programming, Description Logics},
scopus = {2-s2.0-85009168558}
}
@inproceedings{CotZesBel15-ECMLDC-IW,
year = {2015},
booktitle = {Doctoral Consortium of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
editor = {Jaakko Hollmen and Panagiotis Papapetrou },
title = {Structure Learning with Distributed Parameter
Learning for Probabilistic Ontologies},
author = {Giuseppe Cota and Riccardo Zese and Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi},
pages = {75--84},
copyright = {by the authors},
url = {http://urn.fi/URN:ISBN:978-952-60-6443-7},
pdf = {https://aaltodoc.aalto.fi/bitstream/handle/123456789/18224/isbn9789526064437.pdf#page=79},
isbn = {978-952-60-6443-7},
issn = {1799-490X},
issn = {1799-4896},
abstract = {We consider the problem of learning both the structure and
the parameters of Probabilistic Description Logics under DISPONTE.
DISPONTE ("DIstribution Semantics for Probabilistic ONTologiEs")
adapts the distribution semantics for Probabilistic Logic Programming
to Description Logics. The system LEAP for "LEArning Probabilistic
description logics" learns both the structure and the parameters of
DISPONTE knowledge bases (KBs) by exploiting the algorithms CELOE
and EDGE. The former stands for "Class Expression Learning for Ontology
Engineering" and it is used to generate good candidate axioms
to add to the KB, while the latter learns the probabilistic parameters
and evaluates the KB. EDGE for "Em over bDds for description loGics
paramEter learning" is an algorithm for learning the parameters of probabilistic
ontologies from data. In order to contain the computational cost,
a distributed version of EDGE called EDGEMR was developed. EDGEMR
exploits the MapReduce (MR) strategy by means of the Message Passing
Interface. In this paper we propose the system LEAPMR. It is a
re-engineered version of LEAP which is able to use distributed parallel
parameter learning algorithms such as EDGEMR.},
keywords = {Probabilistic Description Logics, Structure Learning,
Parameter Learning, MapReduce, Message Passing Interface}
}
@inproceedings{ZesBel15-OntoLP-IW,
author = {Riccardo Zese and Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi},
title = {Logic Programming Techniques for Reasoning with Probabilistic Ontologies},
booktitle = {
Joint Ontology Workshops 2015, JOWO 2015 - Episode 1: The Argentine Winter of
Ontology; Buenos Aires; Argentina;
25 July 2015 through 27 July 2015},
editor = {Odile Papini and
Salem Benferhat and
Laurent Garcia and
Marie-Laure Mugnier and
Eduardo Fermé and
Thomas Meyer and
Renata Wassermann and
Torsten Hahmann and
Ken Baclawski and
Adila Krisnadhi and
Pavel Klinov and
Stefano Borgo and
Oliver Kutz and
Daniele Porello},
year = {2015},
pdf = {http://ceur-ws.org/Vol-1517/JOWO-15_ontolp_paper_3.pdf},
volume = 1517,
series = {CEUR Workshop Proceedings},
address = {Aachen, Germany},
issn = {1613-0073},
venue = {Buenos Aires, Argentine},
eventdate = {2015-07-25/27},
publisher = {Sun {SITE} Central Europe},
keywords = {Description Logics, Tableau, Prolog, Semantic Web, Pinpoiting Formula},
abstract = {The increasing popularity of the Semantic Web drove to a widespread 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 has to manage non-determinism,
a feature that is hard to handle using such languages. Reasoning on real world domains also requires the capability of managing probabilistic and uncertain information.
We thus present TRILL for ``Tableau Reasoner for descrIption Logics in proLog'' that implements a tableau algorithm and is able to return explanations for the queries and the corresponding probability,
and TRILL$^P$ for ``TRILL powered by Pinpointing formulas'' which is able to compute a Boolean formula representing the set of explanations for the query. This approach can speed up
the process of computing the probability.
Prolog non-determinism is used for easily handling the tableau's non-deterministic expansion rules.},
copyright = {CC0 \url{https://creativecommons.org/publicdomain/zero/1.0/}}
}
@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{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}
}
@inproceedings{AzzRigLam18-PLP-IW,
title = {Modeling Bitcoin Protocols with Probabilistic Logic Programming },
booktitle = {Probabilistic Logic Programming (PLP 2018)},
year = 2018,
author = {Damiano Azzolini and Fabrizio Riguzzi and Evelina Lamma and Elena Bellodi and Riccardo Zese},
editor = {Elena Bellodi and Tom Schrijvers },
volume = {2219},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
address = {Aachen, Germany},
issn = {1613-0073},
url = {http://ceur-ws.org/Vol-2219/paper6.pdf},
venue = {Ferrara, Italy},
eventdate = {September 1, 2018},
copyright = {by the authors},
pages = {49-61},
scopus = {2-s2.0-85054569753}
}
@inproceedings{BelBerGavZes20-RCRA-IW,
title = {Improving the Efficiency of Euclidean {TSP} Solving in {Constraint Programming} by Predicting Effective Nocrossing Constraints},
booktitle = {IPS-RCRA 2020, Italian Workshop on Planning and Scheduling and International Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion},
year = 2020,
author = {Elena Bellodi and Alessandro Bertagnon and Marco Gavanelli and Riccardo Zese},
editor = {Riccardo De Benedictis and Marco Maratea and Andrea Micheli and Enrico Scala and Ivan Serina and Alessandro Umbrico and Mauro Vallati},
volume = {2745},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
address = {Aachen, Germany},
issn = {1613-0073},
venue = {Online Event},
eventdate = {November 25-27, 2020},
copyright = {by the authors},
url = {http://ceur-ws.org/Vol-2745/paper6.pdf},
pages = {1-15}
}
@inproceedings{AzzRigBelLam22-BSCT-IW,
title = {A Probabilistic Logic Model of Lightning Network},
author = {Azzolini, Damiano and Riguzzi, Fabrizio and Bellodi, Elena and Lamma, Evelina},
booktitle = {Business Information Systems Workshops},
year = {2022},
editor = {Abramowicz, Witold and Auer, S{\"o}ren and Str{\'o}{\.{z}}yna, Milena},
pages = {321--333},
series = {Lecture Notes in Business Information Processing (LNBIP)},
publisher = {Springer International Publishing},
address = {Cham, Switzerland},
eventdate = {June 14-17, 2021},
doi = {10.1007/978-3-031-04216-4_28},
url = {https://link.springer.com/chapter/10.1007/978-3-031-04216-4_28},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/AzzRigBelLam22-BSCT-IW.pdf}
}
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