# 2013.bib

@article{DiMFraEta13-IA-IJ,
author = {Nicola Di Mauro and
Paolo Frasconi and
Fabrizio Angiulli and
Davide Bacciu and
Marco de Gemmis and
Floriana Esposito and
Nicola Fanizzi and
Stefano Ferilli and
Marco Gori and
Francesca A. Lisi and
Pasquale Lops and
Donato Malerba and
Alessio Micheli and
Marcello Pelillo and
Francesco Ricci and
Fabrizio Riguzzi and
Lorenza Saitta and
Giovanni Semeraro},
title = {Italian Machine Learning and Data Mining research: The last
years},
journal = {Intelligenza Artificiale},
volume = {7},
number = {2},
year = {2013},
pages = {77-89},
doi = {10.3233/IA-130050},
copyright = {{IOS} Press},
publisher = {{IOS} Press},
abstract = {With the increasing amount of information in electronic form the fields of Machine Learning and Data Mining continue to grow by providing new advances in theory, applications and systems. The aim of this paper is to consider some recent theoretical aspects and approaches to ML and DM with an emphasis on the Italian research.}
}

@article{BalMelRig13-IA-EB,
author = {Matteo Baldoni and Paola Mello and Fabrizio Riguzzi},
title = {Guest-editorial: 25 years of {AI*IA}},
journal = {Intelligenza Artificiale},
year = {2013},
publisher = {{IOS} Press},
doi = {10.3233/IA-130048},
volume = {7},
number = {2},
pages = {69-69},
copyright = {IOS Press}
}

@inproceedings{RigBelLamZes13-AIIA13-IC,
title = {Computing Instantiated Explanations {in~OWL~DL}},
author = { Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and  Riccardo Zese},
booktitle = {Proceedings of the 13th Conference of the Italian Association for Artificial Intelligence ({AI*IA2013}),
Turin, Italy,  4-6 December 2013},
editor = {Matteo Baldoni and
Cristina Baroglio and Guido Boella},
year = {2013},
pages = {397-408},
volume = {8249},
publisher = {Springer},
series = {Lecture Notes in Artificial Intelligence},
address = {Heidelberg, Germany},
note = {The original publication is available at
doi = {10.1007/978-3-319-03524-6_34}
}

@inproceedings{ZesBelLamRig13-CILC13-NC,
title = {A Description Logics Tableau Reasoner in {Prolog}},
author = {Riccardo Zese and Elena Bellodi and Evelina Lamma and  Fabrizio Riguzzi},
booktitle = {Proceedings of the 28th Italian Conference on Computational Logic ({CILC2013}),
Catania, Italy, 25-27 September 2013},
editor = {Domenico Cantone and Marianna Nicolosi Asmundo},
year = {2013},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
issn = {1613-0073},
number = {1068},
address = {Aachen, Germany},
pages = {33-47},
url = {http://ceur-ws.org/Vol-1068/paper-l02.pdf},
copyright = {by the authors}
}

@inproceedings{RigBelLamZese13-RR13b-IC,
title = {{BUNDLE}: A Reasoner for Probabilistic Ontologies},
author = {Fabrizio Riguzzi and Evelina Lamma and Elena Bellodi and Riccardo Zese},
booktitle = {7th International Conference on Web Reasoning and Rule Systems (RR 2013), Mannheim, Germany, July 27-29 2013. Proceedings},
editor = {Faber, Wolfgang and Lembo, Domenico},
year = {2013},
volume = {7994},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
address = {Heidelberg, Germany},
isbn = {978-3-642-39665-6},
pages = {183-197},
doi = {10.1007/978-3-642-39666-3_14},
abstract = {
Representing uncertain information is very important for modeling real world domains.
Recently, the DISPONTE semantics has been proposed for probabilistic description logics.
In DISPONTE, the axioms of a knowledge base can be annotated with a set of variables and a real number between 0 and 1. This real number represents the probability of each version of the axiom in which the specified variables are instantiated.
In this paper we  present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases that follow the $\mathcal{ALC}$ semantics. BUNDLE 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 experiments performed by applying BUNDLE to probabilistic knowledge bases show that it can handle ontologies of realistic size and is competitive with the system PRONTO for the probabilistic description logic P-$\mathcal{SHIQ}$(D).
},
keywords = {Probabilistic Ontologies, Probabilistic Description Logics, OWL, Probabilistic Logic Programming, Distribution Semantics},
note = {The original publication is available at
}

@inproceedings{RigBelLamZese13-RR13a-IC,
author = {Fabrizio Riguzzi and Elena Bellodi and  Evelina Lamma  and Riccardo Zese},
title = {Parameter Learning for Probabilistic Ontologies},
booktitle = {7th International Conference on Web Reasoning and Rule Systems (RR 2013), Mannheim, Germany, July 27-29 2013. Proceedings},
editor = {Faber, Wolfgang and Lembo, Domenico},
year = {2013},
volume = {7994},
isbn = {978-3-642-39665-6},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
address = {Heidelberg, Germany},
pages = {265-270},
doi = {10.1007/978-3-642-39666-3_26},
note = {The original publication is available at
abstract = {Recently, the problem of representing uncertainty in Description Logics (DLs) has received an increasing attention.
In probabilistic DLs, axioms contain numeric parameters that are often difficult to specify or to tune for a human.
In this paper we present an approach for learning and tuning the parameters of
probabilistic ontologies from data. The resulting algorithm,
called EDGE, for Em over bDds for description loGics paramEter learning,
is targeted to DLs following the DISPONTE approach,
that applies the distribution semantics to DLs.},
keywords = {Statistical Relational Learning, Probabilistic Inductive Logic Programming, Probabilistic Logic Programming,  Expectation Maximization, Binary Decision Diagrams,
Logic Programs with Annotated Disjunctions}
}

@incollection{GavRigMilCag13-CIDASD13-BC,
editor = { Ting Yu and Nitesh Chawla and Simeon Simoff},
author = {Marco Gavanelli and Fabrizio Riguzzi and Michela Milano and Paolo Cagnoli},
title = {Constraint and Optimization techniques for supporting  Policy Making},
booktitle = {Computational Intelligent Data Analysis for Sustainable Development},
year = {2013},
series = {Data Mining and Knowledge Discovery Series},
publisher = {Chapman \& Hall/CRC},
chapter = {12},
pages = {361-382},
abstract = {Public institutions develop policies and plans in order to achieve
economic and social development while preserving the environment. This
is a difficult task where computational intelligence data analysis
techniques can provide an important contribution. The policy maker has
to take decisions by optimizing a set of often conflicting objectives
and satisfying a set of constraints. The aim is to reduce negative
impacts and enhance positive impacts of plan decisions on the
environment, society and economy, exploiting all the data that is
available on the territory that is targeted.
Up to now, only agent-based simulation models have been proposed in
the literature for policy making. In these models, agents represent
the parties involved in the decision making and implementation process
and simulation is used in order to evaluate the impacts of the policy.
Agent-based simulation models provide individual level models'': we
claim that the policy planning activity needs also a global
perspective that faces the problem at a global level while tightly
interacting with the individual level model.
We thus propose a mathematical optimization model that can be applied to
regional planning. In the model, decision variables represent
political decisions (for instance the magnitude of a given activity in
the regional plan), potential outcomes are associated with each
decision by considering the available data,  constraints limit possible
combination of assignments of decision variables, and objectives
can be used either to evaluate alternative solutions, or translated
into additional constraints. The model has been solved with Constraint
Programming techniques.
The model has been tested on the Emilia-Romagna regional energy plan.
The results have been validated with an expert in policy making and
impact assessment to evaluate the accuracy of the results.},
url = {http://www.crcnetbase.com/doi/abs/10.1201/b14799-18},
doi = {10.1201/b14799-18},
isbn = {978-1-43-989594-8},
isbn = {978-1-4398-9595-5},
address = {Abingdon, UK}
}

@proceedings{ILP2012-EB,
title = {Inductive Logic Programming, 22nd International Conference, ILP 2012, Dubrovnik, Croatia, September 17-19, 2012, Revised Selected Papers},
year = 2013,
editor = {Fabrizio Riguzzi and Filip  \v{Z}elezn\'{y} },
volume = {7842},
publisher = {Springer},
address = {Heidelberg, Germany},
series = {Lecture Notes in Artificial Intellingence},
issn = {1613-0073},
venue = {Dubrovnik, Croatia},
eventdate = {September 17-19, 2012},
isbn-print = {978-3-642-38811-8},
isbn-online = {978-3-642-38812-5}
}

@proceedings{ILP2012-CEUR-EB,
title = {Late Breaking Papers of the 22nd International Conference on Inductive Logic Programming (ILP)},
year = 2013,
editor = {Fabrizio Riguzzi and Filip  \v{Z}elezn\'{y} },
volume = {975},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
address = {Aachen, Germany},
issn = {1613-0073},
url = {http://ceur-ws.org/Vol-975/},
venue = {Dubrovnik, Croatia},
eventdate = {September 17-19, 2012},
copyright = {by the authors}
}

@article{Rig13-FI-IJ,
author = {Fabrizio Riguzzi},
title = {{MCINTYRE}: A {Monte Carlo} System for Probabilistic Logic Programming},
journal = {Fundamenta Informaticae},
abstract = {Probabilistic Logic Programming is receiving an increasing attention for its ability to model domains with complex and uncertain relations among entities.
In this paper we concentrate on the problem of approximate inference in probabilistic logic programming languages based on the distribution semantics.
A successful approximate approach is based on Monte Carlo sampling, that consists in verifying the truth of the query in a normal program sampled from the probabilistic program.
The ProbLog system includes such an algorithm and so does the cplint suite.
In this paper we propose an approach for Monte Carlo inference that is based on a program transformation that translates a probabilistic program into a normal program to which the query can be posed.  The current sample is stored in the internal database of the Yap Prolog engine.
The resulting system, called MCINTYRE for Monte Carlo INference wiTh Yap REcord, is evaluated on various problems: biological networks, artificial datasets and a hidden Markov model.  MCINTYRE is compared with the Monte Carlo algorithms of ProbLog and  and with the  exact inference  of the PITA system. The results show  that MCINTYRE is faster than the other Monte Carlo systems.},
keywords = {Probabilistic Logic Programming,
Monte Carlo Methods,
Logic Programs with Annotated Disjunctions,
ProbLog},
year = {2013},
publisher = {{IOS} Press},
doi = {10.3233/FI-2013-847},
volume = {124},
number = {4},
pages = {521-541},
copyright = {IOS Press}
}

@article{BelRig13-IDA-IJ,
author = {Elena Bellodi and Fabrizio Riguzzi},
title = { Expectation {Maximization} over Binary Decision Diagrams for Probabilistic Logic Programs},
year = {2013},
volume = {17},
number = {2},
journal = {Intelligent Data Analysis},
publisher = {IOS Press},
copyright = {IOS Press},
pages = {343-363},
doi = {10.3233/IDA-130582},
abstract = {Recently much work in Machine Learning has concentrated on using expressive representation languages that combine aspects of logic and probability. A whole field has emerged, called Statistical Relational Learning, rich of successful applications in a variety of domains.
In this paper we present a Machine Learning technique targeted to Probabilistic Logic Programs, a family of formalisms where uncertainty is represented using Logic Programming tools.
Among various proposals for Probabilistic Logic Programming, the one based on the distribution semantics is gaining popularity and is the basis for languages such as ICL, PRISM, ProbLog and Logic Programs with Annotated Disjunctions.
This paper proposes a technique for learning parameters of these languages. Since their equivalent Bayesian networks contain hidden variables, an Expectation Maximization (EM) algorithm is adopted.
In order to speed the computation up, 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 usage. In particular its speed allows the execution of a high number of restarts, resulting in good  quality of the solutions.},
keywords = {Statistical Relational Learning, Probabilistic Inductive Logic Programming, Probabilistic Logic Programs, Logic Programs with Annotated Disjunctions, Expectation Maximization, Binary Decision Diagrams
}
}

@article{RigSwi13-TPLP-IJ,
author = {Fabrizio Riguzzi and Terrance Swift},
title = {Well\--Definedness and Efficient Inference for Probabilistic Logic Programming under the Distribution Semantics },
year = {2013},
month = {March},
journal = {Theory and Practice of Logic Programming},
editor = { Wolfgang Faber and Nicola Leone},
publisher = {Cambridge University Press},
copyright = {Cambridge University Press},
keywords = {Probabilistic Logic Programming, Possibilistic Logic Programming, Tabling, Answer Subsumption, Program Transformation},
abstract = {The distribution semantics is one of the most prominent approaches for the combination of logic programming and probability theory. Many languages follow this semantics, such as Independent Choice Logic, PRISM, pD, Logic Programs with Annotated Disjunctions (LPADs)  and ProbLog.

When a program contains functions symbols, the distribution semantics
is well\--defined only if the set of explanations for a query is
finite and so is each explanation. Well\--definedness is usually
either explicitly imposed or is achieved by severely limiting the
class of allowed programs.
In this paper we identify a larger class of programs for which the
semantics is well\--defined together with an efficient procedure for
computing the probability of queries.
Since LPADs offer the most general syntax, we present our results for
them, but our results are applicable to all languages under the
distribution semantics.

We present the algorithm Probabilistic Inference with Tabling and
Answer subsumption'' (PITA) that computes the probability of
queries by transforming a probabilistic program into a normal program
and then applying SLG resolution with answer subsumption.
PITA has been implemented in XSB and tested on six domains: two
with function symbols and four without.  The execution times are
compared with those of ProbLog, cplint and
CVE. PITA was almost always able to solve larger problems in a
shorter time, on domains with and without function symbols.},
keywords = {Probabilistic Logic Programming, Tabling, Answer Subsumption, Logic Programs with Annotated Disjunction, Program Transformation},
doi = {10.1017/S1471068411000664},
arxiv = {1110.0631},
pages = {279-302},
volume = {13},
number = {Special Issue 02 - 25th Annual GULP Conference},
scopus = {84874625061},
isi = {000315867300007},
url = {http://arxiv.org/pdf/1110.0631v1}
}


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