# conferences.bib

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@inproceedings{NguRig17-ILP-IC,
title = {Lifted Discriminative Learning of Probabilistic Logic Programs},
author = {Arnaud {Nguembang Fadja} and Fabrizio Riguzzi},
booktitle = {27th International Conference on Inductive Logic Programming, ILP 2017},
year = {2017},
editor = {Nicolas Lachiche and
Christel Vrain},
pdf = {https://ilp2017.sciencesconf.org/data/pages/ILP_2017_paper_10.pdf}
}

@inproceedings{AlbGavLam17-RR-IC,
author = {Alberti, Marco and Gavanelli, Marco
and Lamma, Evelina
and Riguzzi, Fabrizio
and Riccardo, Zese},
editor = {Costantini, Stefania
and Franconi, Enrico
and Van Woensel, William
and Kontchakov, Roman
and Roman, Dumitru},
title = {Dischargeable Obligations in Abductive Logic Programming},
booktitle = {Rules and Reasoning: International Joint Conference,
RuleML+RR 2017, London, UK, July 12--15, 2017, Proceedings},
year = {2017},
publisher = {Springer International Publishing},
copyright = {Springer International Publishing AG},
series = {Lecture Notes in Computer Science},
volume = {10364},
isbn-print = {978-3-319-61251-5},
isbn-online = {978-3-319-61252-2},
doi = {10.1007/978-3-319-61252-2_2},
pdf = {http://mcs.unife.it/~friguzzi/Papers/AlbGavLam-RR17.pdf},
pages = {7--21},
abstract = {
Abductive Logic Programming (ALP) has been proven very
effective for formalizing societies of agents, commitments and norms, in
particular by mapping the most common deontic operators (obligation,
prohibition, permission) to abductive expectations.
In our previous works, we have shown that ALP is a suitable framework
for representing norms. Normative reasoning and query answering were
accommodated by the same abductive proof procedure, named SCIFF.
In this work, we introduce a defeasible
flavour in this framework, in order
to possibly discharge obligations in some scenarios. Abductive expectations
can also be qualified as dischargeable, in the new, extended syntax.
Both declarative and operational semantics are improved accordingly,
and proof of soundness is given under syntax allowedness conditions.
The expressiveness and power of the extended framework, named SCIFFD,
is shown by modeling and reasoning upon a fragment of the Japanese
Civil Code. In particular, we consider a case study concerning manifestations
of intention and their rescission (Section II of the Japanese Civil
Code).},
keywords = {Abduction, Abductive Logic Programming, Legal Reasoning,
Normative Reasoning},
note = {The final publication is available at Springer via
\url{http://dx.doi.org/10.1007/978-3-319-61252-2_2}}
}

@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},
series = {Lecture Notes in Computer Science},
volume = {10180},
pdf = {http://mcs.unife.it/~friguzzi/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},
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{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},
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},
series = {Lecture Notes in Computer Science},
volume = {10037},
copyright = {Springer International Publishing AG},
keywords = {Probabilistic Logic Programming, Probabilistic Logical Inference, Hybrid program},
pdf = {http://mcs.unife.it/~friguzzi/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{AlbLamRigZes16-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 Hybrid Knowledge Bases under the Distribution
Semantics},
author = {Marco Alberti and Evelina Lamma and Fabrizio Riguzzi and Riccardo Zese},
abstract = {Since Logic Programming (LP) and Description Logics (DLs) are based on
different assumptions (the closed and the open world assumption,
respectively), combining them provides higher expressiveness in
applications  that require both
assumptions.

Several proposals have been made to combine LP and DLs. An especially
successful line of research is the one based on the Lifschitz's
logic of Minimal Knowledge with Negation as Failure (MKNF).  Motik
and Rosati introduced Hybrid knowledge bases (KBs), composed of LP
rules and DL axioms, gave them an MKNF semantics and
studied their complexity. Knorr et al. proposed a well-founded semantics for
Hybrid KBs where the LP clause heads are non-disjunctive, which
keeps querying polynomial (provided the underlying DL is polynomial)
even when the LP portion is non-stratified.

In this paper, we propose Probabilistic Hybrid Knowledge Bases (PHKBs),
where the atom in the head of LP clauses and each DL axiom is
annotated with a probability value. PHKBs are given a distribution
semantics by defining a probability distribution over deterministic
Hybrid KBs. The probability of a query being true is the sum of the
probabilities of the deterministic KBs that entail the query. Both
epistemic and statistical probability can be addressed, thanks to
the integration of probabilistic LP and DLs.},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
volume = {10037},
copyright = {Springer International Publishing AG},
issn = {0302-9743},
keywords = {Probabilistic Logic Programming, Probabilistic Description Logics, Hybrid Knowledge Bases},
pdf = {http://mcs.unife.it/~friguzzi/Papers/AlbLamRig-AIXIA16.pdf},
doi = {10.1007/978-3-319-49130-1_27},
pages = {364-376},
venue = {Genova, Italy},
scopus = {2-s2.0-85005950065},
wos = {WOS:000389797400027},
eventdate = {November 28-December 1, 2016},
isbn-online = {978-3-319-49129-5},
isbn-print = {978-3-319-49130-1},
note = {The final publication is available at Springer via
\url{http://dx.doi.org/10.1007/978-3-319-49130-1_27}}
}

@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://mcs.unife.it/~friguzzi/Papers/CotZesBel-ILP15.pdf},
year = {2016},
publisher = {Springer International Publishing},
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}}
}

@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},
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{RigBel15-IJCAI-IC,
author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence,
Buenos Aires, Argentina, 25-31 July 2015},
title = {Reasoning with Probabilistic Ontologies},
year = {2015},
editor = {Qiang Yang and Michael Wooldridge},
pages = {4310-4316},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
address = {Palo Alto, California USA},
copyright = {International Joint Conferences on Artificial Intelligence },
isbn = {978-1-57735-738-4},
pdf = {http://ijcai.org/papers15/Papers/IJCAI15-613.pdf},
keywords = {Probabilistic Ontologies, Probabilistic Description Logics, OWL, Probabilistic Logic Programming, Distribution Semantics},
abstract = {Modeling real world domains requires ever more frequently to represent uncertain information.
The DISPONTE semantics for probabilistic description logics allows to annotate axioms of a knowledge base with a value that represents their probability.
In this paper we discuss approaches for performing inference
from probabilistic ontologies following the DISPONTE semantics.
We present the algorithm BUNDLE for computing the probability of queries.
BUNDLE exploits an underlying  Description Logic reasoner, such as Pellet, in order to find explanations for a query. These are then encoded in a Binary Decision Diagram that is
used for computing the probability of the query.},
issn = {10450823}
}

@inproceedings{RigSwi14-ILP11-IC,
author = {Fabrizio Riguzzi and Terrance Swift},
editor = {Muggleton, Stephen H.  and Watanabe, Hiroaki},
title = {The {PITA} System for Logical-Probabilistic Inference},
booktitle = {Latest Advances in Inductive Logic Programming, Inductive Logic Programming,
21th International Conference, ILP 2011, Late Breaking papers, London, UK, 31 July-3 August, 2011 },
year = {2014},
publisher = {World Scientific},
isbn = {978-1-78326-508-4},
isbn-ebook = {978-1-78326-510-7},
isbn-ebook-institutions = {978-1-78326-509-1},
doi = {10.1142/9781783265091_0010},
url = {http://mcs.unife.it/~friguzzi/Papers/RigSwi12-ILP11-IC.pdf},
pages = {79-86},
abstract = {Introduction,
Probabilistic Logic Programming,
The PITA System,
Experiments,
Bibliography},
keywords = {Logic Programming, Probabilistic Logic Programming,
Inductive Logic Programming},
scopus = {2-s2.0-84988663635}
}

@inproceedings{RigBelLamZes14-ILP13-IC,
author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese},
title = {Learning the Parameters of Probabilistic Description Logics},
booktitle = { Late Breaking papers of the 23rd International Conference on Inductive Logic Programming,
Rio de Janeiro, Brazil,  August 28th to 30th, 2013},
editor = {Gerson Zaverucha and Santos Costa, Vitor and Aline Marins Paes},
year = {2014},
volume = {1187},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
issn = {1613-0073},
url = {http://ceur-ws.org/Vol-1187/paper-08.pdf},
pages = {46-51},
}

@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},
pdf = {http://mcs.unife.it/~friguzzi/Papers/RigBelLamZes-AIIA13.pdf},
note = {The original publication is available at
doi = {10.1007/978-3-319-03524-6_34}
}

@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},
isbn = {978-3-642-39665-6},
pages = {183-197},
doi = {10.1007/978-3-642-39666-3_14},
pdf = {http://mcs.unife.it/~friguzzi/Papers/RigBelLam-RR13b.pdf},
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},
pages = {265-270},
doi = {10.1007/978-3-642-39666-3_26},
pdf = {http://mcs.unife.it/~friguzzi/Papers/RigBelLam-RR13a.pdf},
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}
}

@inproceedings{GavRiguMilCag12-ISAIM12-IC,
author = {Marco Gavanelli and
Fabrizio Riguzzi and
Michela Milano and
Paolo Cagnoli},
title = {Constraint and Optimization techniques for supporting Policy
Making},
booktitle = {International Symposium on Artificial Intelligence and Mathematics
(ISAIM 2012), Fort Lauderdale, Florida, USA, January 9-11,
2012},
year = {2012},
pdf = {http://www.cs.uic.edu/pub/Isaim2012/WebPreferences/ISAIM2012_Gavanelli_etal.pdf}
}

@inproceedings{BelRig12-ILP11-IC,
author = {Elena Bellodi and Fabrizio Riguzzi},
title = {Learning the Structure of Probabilistic Logic Programs},
booktitle = {Inductive Logic Programming
21st International Conference, ILP 2011, London, UK, July 31 - August 3, 2011. Revised Papers },
year = {2012},
editor = {Muggleton, Stephen H. and Tamaddoni-Nezhad, Alireza and Lisi, Francesca A.},
doi = {10.1007/978-3-642-31951-8_10},
series = {LNCS},
volume = {7207},
publisher = {Springer},
pages = {61-75},
note = {The original publication is available at \url{http://www.springerlink.com}},
pdf = {http://mcs.unife.it/~friguzzi/Papers/BelRig12-ILP11-IC.pdf},
keywords = {Probabilistic Inductive Logic Programming, Logic Programs with Annotated Disjunctions, ProbLog},
abstract = {There is a growing interest in the field of
Probabilistic Inductive Logic Programming, which uses languages that
integrate logic programming and probability.
Many of these languages are based on the distribution semantics and recently various authors have proposed systems for learning the parameters (PRISM, LeProbLog, LFI-ProbLog
and EMBLEM) or both the structure and the parameters (SEM-CP-logic) of these languages.
EMBLEM for example uses an Expectation Maximization approach in which  the expectations are computed on Binary Decision Diagrams.
In this paper we present the algorithm SLIPCASE for Structure LearnIng of ProbabilistiC logic progrAmS with Em over bdds''. It performs a beam search in the space of the language of Logic Programs with Annotated Disjunctions (LPAD) using the log likelihood of the data as the guiding heuristics. To estimate the log likelihood of theory refinements it performs a limited number of Expectation Maximization iterations of EMBLEM.
SLIPCASE has been tested on three real world datasets and compared with SEM-CP-logic and  Learning using Structural Motifs, an algorithm for Markov Logic Networks. The results show that SLIPCASE achieves higher areas under the precision-recall and ROC curves and is more scalable.
}
}

@inproceedings{GavRigMil11-AIIA11-IC,
author = {Marco Gavanelli and Fabrizio Riguzzi  and Michela Milano and Davide Sottara and Alessandro Cangini and Paolo Cagnoli},
title = {An Application of Fuzzy Logic to Strategic Environmental Assessment},
booktitle = {Proceedings of the 12th Congress of the Italian Association for Artificial Intelligence, Palermo,  15-17 September 2011 },
year = {2011},
editor = {Pirrone, Roberto and Sorbello, Filippo},
publisher = {Springer},
pdf = {http://mcs.unife.it/~friguzzi/Papers/GavRigMil-AIIA11.pdf},
series = {Lecture Notes in Artificial Intelligence},
volume = {6934},
abstract = {Strategic Environmental Assessment (SEA) is used to evaluate the environmental effects of regional plans and programs.
SEA expresses dependencies between  plan activities (infrastructures, plants, resource extractions, buildings, etc.) and environmental pressures, and between these and environmental receptors.
In this paper we employ fuzzy logic and many-valued logics together with numeric transformations for performing SEA. In particular, we discuss four  models that
capture  alternative interpretations of the dependencies,  combining quantitative and qualitative  information.
We have tested the four models and presented the results to the expert for validation.
The  interpretability of the results of the models was appreciated by the expert that liked in particular those models returning a possibility distribution in place of a crisp result.
},
keywords = {Strategic Environmental Assessment, Regional Planning, Fuzzy Logic},
doi = {10.1007/978-3-642-23954-0_30},
pages = {324-335},
note = {The original publication is available at \url{http://www.springerlink.com}}
}

@inproceedings{BraRig10-ILP10-IC,
author = {Stefano Bragaglia and Fabrizio Riguzzi},
title = {Approximate Inference for
Logic Programs with Annotated Disjunctions},
booktitle = {Inductive Logic Programming
20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010. Revised Papers },
volume = {6489},
pages = {30--37},
year = {2011},
series = {LNCS},
editor = {Frasconi, Paolo and Lisi, Francesca},
publisher = {Springer},
doi = {10.1007/978-3-642-21295-6_7},
url = {http://mcs.unife.it/~friguzzi/Papers/BraRig-ILP10.pdf},
note = {The original publication is available at \url{http://www.springerlink.com}},
abstract = {Logic Programs with Annotated Disjunctions (LPADs) are a promising language for Probabilistic Inductive Logic Programming.
In order to develop efficient learning systems for LPADs, it is fundamental to have high\--performing inference algorithms. The existing approaches take too long or fail for large problems. In this paper we adapt to LPAD the approaches for approximate inference that have been developed for ProbLog, namely $k$\--best and Monte Carlo.

$k$\--Best finds a lower bound of the probability of a query by identifying the $k$ most probable explanations while Monte Carlo estimates the probability by smartly sampling the space of programs.
The two techniques have been implemented in the \texttt{cplint} suite and have been tested on real and artificial datasets representing graphs. The results show that both algorithms are able to solve larger problems often in less time than the exact algorithm.},
keywords = {Probabilistic Inductive Logic Programming, Logic Programs with Annotated Disjunctions, ProbLog},
scopus = {2s2.079959303593},
wos = {WOS:000325925600007},
isbn = {9783642212949}
}

@inproceedings{BelRigLam10-KSEM10-IC,
author = {Elena Bellodi and Fabrizio Riguzzi and  Evelina Lamma},
title = {Probabilistic Declarative Process Mining},
booktitle = {Proceedings of the 4th International Conference on Knowledge Science, Engineering \& Management ({KSEM 2010}),
Belfast,  UK, September 1-3, 2010},
year = {2010},
editor = {Bi, Yaxin and Williams, Mary-Anne},
abstract = {
The management of business processes is receiving much attention, since it can
support signicant eciency improvements in organizations. One of the most
interesting problems is the representation of process models in a language that
allows to perform reasoning on it. Various knowledge-based languages have been
lately developed for such a task and showed to have a high potential due to the
In this work we present an approach for the automatic discovery of knolwedge-
based process models expressed by means of a probabilistic logic, starting from
a set of process execution traces. The approach first uses the DPML (Declarative
Process Model Learner) algorithm to extract a set of integrity constraints from
a collection of traces. Then, the learned constraints are translated into Markov
Logic formulas and the weights of each formula are tuned using the Alchemy
system. The resulting theory allows to perform probabilistic classication of
traces. We tested the proposed approach on a real database of university
students' careers. The experiments show that the combination of DPML and Alchemy
achieves better results than DPML alone.},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {6291},
pages = {292--303},
doi = {10.1007/978-3-642-15280-1_28},
url = {http://mcs.unife.it/~friguzzi/Papers/BelRIgLam-KSEM10.pdf},
note = {The original publication is available at \url{http://www.springerlink.com}}
}

@inproceedings{RigSwi10-ICLP10-IC,
author = {Fabrizio Riguzzi and Terrance Swift},
title = {{T}abling and {A}nswer {S}ubsumption for {R}easoning on {L}ogic {P}rograms with {A}nnotated {D}isjunctions},
booktitle = {Technical Communications of the 26th Int'l.
Conference on Logic Programming (ICLP'10)},
volume = {7},
year = {2010},
editor = {M.~Hermenegildo and T.~Schaub},
month = jul,
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
series = {Leibniz International Proceedings in
Informatics (LIPIcs)},
isbn = {978-3-939897-17-0},
issn = {1868-8969},
Derivative Works 3.0},
abstract = {The paper presents  the algorithm Probabilistic
Inference with Tabling and Answer subsumption'' (PITA) for
computing the probability of queries from Logic Programs with Annotated Disjunctions.
PITA is based on a program transformation techniques that adds an extra argument
to every atom. PITA uses tabling for saving intermediate results and
subgoal.
PITA has been implemented in XSB and compared with the ProbLog,
cplint and CVE systems. The results show that in almost all
cases, PITA is able to solve larger problems and is faster than
competing algorithms.},
keywords = {Probabilistic Logic Programming, Tabling, Answer Subsumption, Logic Programs with Annotated Disjunction, Program Transformation},
pages = {162--171},
url = {http://drops.dagstuhl.de/opus/volltexte/2010/2594/},
doi = {10.4230/LIPIcs.ICLP.2010.162}
}

@inproceedings{Rig08-ICLP08-IC,
author = {Fabrizio Riguzzi},
title = {Inference with Logic Programs with Annotated Disjunctions under the Well Founded Semantics},
booktitle = {Logic Programming, 24th International Conference, ICLP 2008,
Udine, Italy, December  9-13, 2008, Proceedings},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
year = {2008},
note = {The original publication is available at \url{http://www.springerlink.com}},
volume = {5366},
pages = {667-771},
doi = {10.1007/978-3-540-89982-2_54},
pdf = {http://mcs.unife.it/~friguzzi/Papers/Rig-ICLP08.pdf},
}

@inproceedings{LamMelRigSto-ILP07-IC,
author = {
Evelina Lamma and Paola Mello and Fabrizio Riguzzi and Sergio Storari},
title = {Applying Inductive Logic Programming to Process  Mining},
booktitle = {Proceedings of the 17th International Conference on Inductive Logic Programming},
year = {2008},
publisher = {Springer},
abstract = {The management of business processes has recently received a lot of attention. One of the most interesting problems is the description of a process model in a language that allows the checking of the compliance of a process execution (or trace) to the model. In this paper we propose a language  for the representation of process models that is inspired to the SCIFF language and is an extension of clausal logic.
A process model is represented in the language as a set of integrity constraints that allow conjunctive formulas as disjuncts in the head.
We present an approach for inducing these models from data: we define a subsumption relation for the integrity constraints, we define a refinement operator and we adapt the algorithm ICL to the problem of learning such formulas. The system has been applied to the problem of inducing the model of a sealed bid auction and of the NetBill protocol. The data used for learning and testing were randomly generated from correct models of the processes.},
keywords = {Process Mining, Learning from Interpretations, Business Processes, Interaction Protocols},
series = {Lecture Notes in Artificial Intelligence},
volume = {4894},
note = {The original publication is available at \url{http://www.springerlink.com}},
pages = {132--146},
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamMelRigSto-ILP07.pdf},
doi = {10.1007/978-3-540-78469-2_16},
}

@inproceedings{Rig-ILP06-IC,
author = {
Fabrizio Riguzzi},
title = {{ALLPAD}: Approximate Learning of Logic Programs with Annotated Disjunctions},
booktitle = {Proceedings of the 16th International Conference on Inductive Logic Programming},
year = {2007},
editor = {Stephen Muggleton and Ramon Otero},
publisher = {Springer},
series = {Lecture Notes in Artificial Intelligence},
abstract = {In this paper we present the system ALLPAD for learning Logic Programs with Annotated Disjunctions (LPADs).
ALLPAD modifies  the  previous system LLPAD in order to  tackle real world learning problems more effectively.
This is achieved by looking for an approximate solution rather than a perfect one. ALLPAD has been tested on the
problem of classifying proteins according  to their tertiary structures and the results compare favorably with
most other approaches.},
note = {The original publication is available at \url{http://www.springerlink.com}},
volume = {4455},
pages = {43--45},
abstract = {In this paper we present the system ALLPAD for learning Logic Programs with Annotated Disjunctions (LPADs). ALLPAD modifies  the  previous system LLPAD in order to  tackle real world learning problems more effectively. This is achieved by looking for an approximate solution rather than a perfect one. ALLPAD has been tested on the problem of classifying proteins according  to their tertiary structures and the results compare favorably with most other approaches.},
keywords = {Logic Programs with Annotated Disjunctions, Statistical Relational Learning},
pdf = {http://mcs.unife.it/~friguzzi/Papers/Rig-ILP06.pdf},
doi = {10.1007/978-3-540-73847-3_11},
}

@inproceedings{LamMelMonRigSto-BPM07-IC,
author = {
Evelina Lamma and Paola Mello and Marco Montali and Fabrizio Riguzzi and Sergio Storari},
title = {Inducing Declarative Logic-Based Models from Labeled Traces},
booktitle = {Proceedings of the 5th International Conference on Business Process Management},
year = {2007},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamMelMon-BPM07.pdf},
doi = {10.1007/978-3-540-75183-0_25},
volume = {4714},
pages = {344--359},
note = {The original publication is available at \url{http://www.springerlink.com}}
}

@inproceedings{Rig-AIIA07-IC,
author = {
Fabrizio Riguzzi },
title = {A Top Down Interpreter for {LPAD} and {CP}\--logic},
booktitle = {Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence},
year = {2007},
publisher = {Springer},
pdf = {http://mcs.unife.it/~friguzzi/Papers/Rig-AIIA07.pdf},
series = {Lecture Notes in Artificial Intelligence},
volume = {4733},
note = {The original publication is available at \url{http://www.springerlink.com}},
pages = {109--120},
abstract = {Logic Programs with Annotated Disjunctions and CP-logic are two different but related languages for expressing probabilistic information in logic programming.
The paper presents a top down interpreter for computing the probability of a query from a program in one of these two languages.
The algorithm is based on the one available for ProbLog.
The performances of the algorithm are compared with those of a Bayesian reasoner and with those of the ProbLog interpreter. On programs that have a small grounding, the Bayesian reasoner is more scalable, but programs with a large grounding require the top down interpreter. The comparison with ProbLog shows that the added expressiveness effectively requires more computation resources.},
doi = {10.1007/978-3-540-74782-6_11 },
keywords = {Probabilistic Logic Programming, Logic Programs with Annotated Disjunction, Probabilistic Reasoning},
scopus = {2s2.038049160383},
wos = {WOS:000250857400009},
isbn = {9783540747819},
issn = {03029743}
}

@inproceedings{GamLamRig05-IDA05-IC,
author = {Giacomo Gamberoni and Evelina Lamma and Fabrizio Riguzzi and  Sergio Storari and Stefano Volinia},
title = {Bayesian Networks Learning for Gene Expression Datasets},
booktitle = {Advances in Intelligent Data Analysis VI: 6th International Symposium on Intelligent Data Analysis, {IDA} 2005, Madrid, Spain, \September\  8-10, 2005. Proceedings},
year = {2005},
publisher = {Springer Verlag},
month = sep,
series = {Lecture Notes in Computer Science},
volume = {3646},
note = {The original publication is available at \url{http://www.springerlink.com}},
pages = {109--120},
isbn = {3-540-28795-7},
issn = {0302-9743},
doi = {10.1007/11552253_11},
http = {http://dx.medra.org/10.1007/11552253_11},
pdf = {http://mcs.unife.it/~friguzzi/Papers/GamLamRig-IDA05.pdf},
}

@inproceedings{LamRigSto04-IPMU04-IC,
author = {Evelina Lamma and Fabrizio Riguzzi and Sergio Storari},
title = {Improving the K2 Algorithm Using Association Rules Parameters},
booktitle = {Information Processing and Management of Uncertainty in Knowledge-Based Systems
({IPMU}2004), Perugia, 4-9 July 2004},
editor = { B. Bouchon-Meunier and G. Coletti and R. R. Yager},
abstract = {A Bayesian network is an appropriate tool to work with a sort of uncertainty
and probability, that are typical of real-life applications. Bayesian network arcs represent
statistical dependence between different variables.  In the data mining field, association
rules can be interpreted as well as expressing statistical dependence relations.
K2 is a well-known algorithm which is able to learn   Bayesian network.  In this paper we
want to  present an extension of K2 called K2-rules that exploits a parameter normally
defined in relation to association rules for learning  Bayesian networks.
The experiments performed show that K2-rules improves K2 with respect to both
the quality of the learned network and the execution
time. },
year = {2004},
month = jul,
publisher = {Editrice Universit\{a} La Sapienza},
pages = {1667--1674},
url = {http://ipmu2004.dipmat.unipg.it/},
keywords = {Bayesian Networks Learning},
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamRigSto-IPMU04.pdf}
}

@inproceedings{LamRigSto04-ECAI04-IC,
author = {Evelina Lamma and Fabrizio Riguzzi and Sergio Storari},
title = {Exploiting Association and Correlation Rules Parameters for Improving the K2 Algorithm},
booktitle = {Proceedings of the 16th Eureopean Conference on Artificial Intelligence, ECAI 2004, including Prestigious Applicants
of Intelligent Systems, PAIS 2004, Valencia, Spain, August
22-27, 2004},
editor = {Ramon Lopez de Mantaras and Lorenza Saitta},
abstract = {A Bayesian network is an appropriate tool to deal with the uncertainty that
is typical of real-life applications. Bayesian network arcs represent statistical dependence
between different variables.  In the data mining field, association and correlation rules
can be interpreted as well as expressing statistical dependence relations.
K2 is a well-known algorithm which is able to learn Bayesian network.
In this paper we present two extensions of K2 called K2-Lift and K2-X2 that
exploit two parameters normally defined in relation to association and correlation rules for
learning Bayesian networks.  The experiments performed show that K2-Lift and K2-X2 improve K2
with respect to both the quality of the learned network and the execution time.},
year = {2004},
month = aug,
publisher = {{IOS} Press},
pages = {500-504},
keywords = {Bayesian Networks Learning},
isbn = {1-58603-452-9},
isbn = {9781586034528},
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamRigSto-ECAI04.pdf},
url = {http://www.frontiersinai.com/ecai/ecai2004/ecai04/pdf/p0500.pdf},
series = {Frontiers in Artificial Intelligence and Applications},
issn = {0922-6389},
volume = {110}
}

@inproceedings{Rig04-ILP04-IC,
author = {Fabrizio Riguzzi},
title = {Learning Logic Programs with Annotated Disjunctions},
booktitle = {Inductive Logic Programming: 14th International Conference, ILP 2004, Porto, Portugal, September 6-8, 2004. Proceedings},
editor = {A Srinivasan and R. King},
abstract = {Logic Programs with Annotated Disjunctions (LPADs) provide a
simple and elegant framework for integrating probabilistic
reasoning and logic programming. In this paper we propose an
algorithm for learning LPADs. The learning problem we consider
consists in starting from a sets of interpretations annotated
with their probability and finding one (or more) LPAD that assign
to each interpretation the associated probability. The learning
algorithm first finds all the disjunctive clauses that are true
in all interpretations, then it assigns to each disjunct in the
head a probability and finally decides how to combine the clauses
to form an LPAD by solving a constraint satisfaction problem. We
show that the learning algorithm is correct and complete.},
year = {2004},
month = sep,
publisher = {Springer Verlag},
keywords = {Logic Programming, Inductive Logic Programming, Probabilistic Logic
Programming},
series = {Lecture Notes in Artificial Intelligence},
volume = {3194},
pages = {270--287},
note = {The original publication is available at \url{http://www.springerlink.com}},
pdf = {http://mcs.unife.it/~friguzzi/Papers/Rig-ILP04.pdf},
isbn = {3-540-22941-8},
issn = {0302-9743},
doi = {10.1007/978-3-540-30109-7_21},
wos = {WOS:000223999400021},
scopus = {2s2.022944492698}
}

@inproceedings{LamRigSta03-AI*IA03-IC,
author = {Evelina Lamma and Fabrizio Riguzzi and Andrea Stambazzi and Sergio Storari},
title = {Improving the {SLA} algorithm using association rules},
booktitle = {{AI*IA} 2003: Advances in Artificial Intelligence: 8th Congress of the Italian Association for Artificial Intelligence Pisa, Italy, September 23-26, 2003 Proceedings},
editor = {Amedeo Cappelli and Franco Turini},
abstract = {A bayesian network is an appropriate tool for working
with uncertainty and probability, that are typical of real-life
applications. In literature we find different approaches for
bayesian network learning. Some of them are based on search and
score methodology and the others follow an information theory
based approach. One of the most known algorithm for learning
bayesian network is the SLA algorithm. This algorithm constructs
a bayesian network by analyzing conditional independence
relationships among nodes. The SLA algorithm has three phases:
drafting, thickening and thinning. In this work, we propose an
alternative method for performing the drafting phase. This new
methodology uses data mining techniques, and in particular the
computation of a number of parameters usually defined in relation
to association rules, in order to learn an initial structure of a
bayesian network. In this paper, we present the BNL-rules
algorithm (Bayesian Network Learner with association rules) that
exploits a number of  association rules parameters to infer the
structure of a bayesian network. We will also present the
comparisons  between SLA and BNL-rules algorithms on learning
four bayesian networks. },
year = {2003},
month = sep,
publisher = {Springer Verlag},
series = {{Lecture Notes on Artificial Intelligence}},
volume = {2829},
note = {The original publication is available at \url{http://www.springerlink.com}},
pages = {165--175},
keywords = {Bayesian Networks Learning},
issn = {0302-9743},
doi = {10.1007/b13658},
isbn = {3-540-20119-X},
http = {http://dx.medra.org/10.1007/b13658},
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamRigSta-AIIA03.pdf},
}

@inproceedings{LamRigPer02-CMSRA02-IC,
author = {Evelina Lamma and Fabrizio Riguzzi and Lu\'\i{}s Moniz Pereira},
title = {Belief Revision via {L}amarckian Evolution},
booktitle = {Second International Workshop on Computational Models of Scientific
Reasoning And Applications (II CMSRA) held at IC-AI 2002 Conference, Monte Carlo
Resort, Las Vegas, Nevada, USA, June 27, 2002},
editor = {Claudio Delrieux},
abstract = {We present a genetic algorithm for performing belief revision in
a multi-agent environment. In this setting, different individuals
are exposed to different experiences. This may happen because the
world surrounding an agent changes over time or because  we allow
agents exploring different parts of the world. The algorithm
permits the exchange of chromosomes from different agents and
combines two different evolution strategies, one based on
Darwin's and the other  on Lamarck's evolutionary theory.
Experiments on a problem of digital circuit diagnosis and on the
$n$-queen problem show that the addition of the Lamarckian
operator in the single agent case improves the fitness of the
best solution, even if in the digital circuit case the fitness
difference is not significant. Moreover, the experiments show
that the distribution of constraints, even if it leads to a
decrease of the fitness of the best solution, does not produce a
significant difference.},
year = {2002},
publisher = {CSREA},
keywords = {Genetic Algorithms,Theory Revision},
month = jun,
pages = {1--7},
http = {http://www.lip.uns.edu.ar/cmsra/lamma.pdf},
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamRigPer-CMSRA02.pdf}
}

@inproceedings{LamModRig02-CBMS02-IC,
author = {Evelina  Lamma and Giuseppe Modestino and Fabrizio Riguzzi and Sergio Storari
and Paola Mello and Annamaria Nanetti},
title = {An Intelligent Medical System for Microbiological Data
Validation and Nosocomial Infection Surveillance},
booktitle = {The 15th International Conference on Computer Based Medical Systems
(CBMS 2002), Maribor, Slovenia, 4-7 June 2002},
year = {2002},
publisher = {{IEEE} Press},
address = {Los Alamitos, California,  \USA},
pages = {13--20},
month = jun,
editor = {Kokol, P. and Stiglic, B. and Zorman, M. and Zazula, D. },
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamRigPer-CBMS02.pdf},
doi = {10.1109/CBMS.2002.1011348},
isbn = {0769516149}
}

@inproceedings{LamRigPer01-EVOLEARN01-IC,
author = {Evelina Lamma and Fabrizio Riguzzi and Lu\'\i{}s Moniz Pereira},
title = {Belief Revision by {L}amarckian Evolution},
booktitle = {Applications of Evolutionary Computing : EvoWorkshops 2001: EvoCOP, EvoFlight, EvoIASP, EvoLearn, and EvoSTIM, Como, Italy, April 18-20, 2001, Proceedings},
editor = {E.J.W. Boers and J. Gottlieb and P.L. Lanzi and R.E. Smith and S. Cagnoni and E. Hart and G.R. Raidl and H. Tijink},
publisher = {Springer-Verlag},
note = {The original publication is available at \url{http://www.springerlink.com}},
series = {Lecture Notes on Computer Science},
abstract = {We propose a  multi-agent  genetic algorithm to accomplish
belief revision. The algorithm implements  a new evolutionary
strategy  resulting from a combination of Darwinian and
Lamarckian approaches. Besides encompassing the Darwinian
operators of selection, mutation and crossover, it comprises a
Lamarckian operator that mutates the genes in a chromosome that
code for the believed assumptions. These self mutations are
performed as a consequence of the chromosome phenotype's
experience obtained while solving a belief revision problem.
They are directed by a belief revision procedure which relies on
tracing the logical derivations leading to inconsistency of
belief, so as to remove the latter's support on the gene coded
assumptions, by mutating the genes.},
volume = {2037},
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamPerRig-EVOLEARN01.pdf},
year = {2001},
month = apr,
pages = {404–413},
keywords = {Genetic_Algorithms,Theory_Revision},
issn = {0302-9743},
doi = {10.1007/3-540-45365-2_42},
scopus = {2s2.084958052631},
wos = {WOS:000174203500042},
isbn = {3540419209}
}

@inproceedings{LamMelNan01-ISMDA01-IC,
author = {Evelina Lamma and Paola Mello and Annamaria Nanetti and Gianluca Poli and
Fabrizio Riguzzi and Sergio Storari},
title = {An Expert System for Microbiological Data Validation and Surveillance},
booktitle = {Medical Data Analysis: Second International Symposium, {ISMDA} 2001 Madrid, Spain, October 8-9, 2001 Proceedings},
volume = {2199},
abstract = {In this work, we describe a system for microbiological laboratory data
validation and bacteria infections monitoring. In the following sections we
report about the first results we have obtained with a prototype that adopts a
knowledge-base approach for identifying critical situations and correspondingly
issuing alarms. The knowledge base has been obtained from international
standard guidelines for microbiological laboratory practice and from expert
suggestions.},
keywords = {Expert Systems, Microbiology},
series = {{Lecture Notes on Computer Science}},
publisher = {Springer Verlag},
note = {The original publication is available at \url{http://www.springerlink.com}},
year = {2001},
pages = {153--160},
month = oct,
editor = {Crespo, J. and Maojo, V.and  Martin, F.},
isbn = {3-540-42734-1},
issn = {0302-9743},
doi = {10.1007/3-540-45497-7},
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamMelNan-ISMDA01.pdf},
scopus = {2-s2.0-0005697191}
}

@inproceedings{LamManMel00-ISMDA00-IC,
title = {A System for Monitoring Nosocomial Infections},
author = {Evelina Lamma and Marco Manservigi and Paola Mello and Roberto Serra and Sergio Storari
and Fabrizio Riguzzi},
booktitle = {Medical Data Analysis: First International Symposium, {ISMDA} 2000, Frankfurt, Germany, September 29-30, 2000. Proceedings},
editor = {R. W. Brause and E. Hanisch },
publisher = {Springer Verlag},
abstract = {In this work, we describe a project, jointly started by DEIS
University of Bologna and Dianoema S.p.A., in order to build a system which is
able to monitor nosocomial infections. To this purpose, the system computes
various statistics that are based on the count of patient infections over a period
of time. The precise count of patient infections needs a precise definition of
bacterial strains that is found by applying clustering to data on past infections.
Moreover, the system is able to identify critical situations for a single patient
(e.g., unexpected antibiotic resistance of a bacterium) or for hospital units (e.g.,
contagion events) and alarm the microbiologist.},
keywords = {Knowledge-based Systems; Nosocomial Infections},
pages = {282--292},
month = sep,
year = {2000},
series = {{Lecture Notes on Computer Science}},
volume = {1933},
note = {The original publication is available at \url{http://www.springerlink.com}},
issn = {0302-9743},
doi = {10.1007/3-540-39949-6_34},
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamManMel-ISMDA00.pdf},
scopus = {2-s2.0-33646094404},
wos = {WOS:000171225400034},
isbn = {3540410899}
}

@inproceedings{MilOmiRig99-FLAIRS99-IC,
author = {Michela Milano and Andrea Omicini and Fabrizio Riguzzi},
title = {Adopting an Object-Oriented Data Model in Inductive Logic Programming},
booktitle = {Proceedings of the 12th International FLAIRS Conference (FLAIRS99),  3--5 May 1999, Orlando, Florida, USA},
editor = {Amruth N. Kumar and Ingrid Russell},
publisher = {{AAAI}},
year = 1999,
month = may,
pages = {273--279},
keywords = {Knowledge Representation},
pdf = {http://mcs.unife.it/~friguzzi/Papers/MilOmRig-FLAIRS99.pdf}
}

@inproceedings{LamMelRig98-PAP98-IC,
author = { Evelina Lamma and Paola Mello and Fabrizio Riguzzi},
title = {A System for Measuring Function Points},
booktitle = {Proceedings of the 6th International Conference on Practical Applications
of Prolog and 4th International Conference on Practical Applications of Constraint Technology
(PAPPACT98), London, March 1998},
publisher = {The Practical Application Company Ltd.},
year = 1998,
pages = {41--60},
month = mar,
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamMelRig-PAP98.pdf},
abstract = {We present the system FUN for measuring the Function Point software metric from specifications expressed in the form of an Entity Relationship (ER) diagram and a Data Flow Diagram (DFD). As a first step towards the implementation of the system, the informal Function Point counting rules have been translated into rigorous rules expressing properties of the ERDFD. Prolog was chosen for the implementation because of its declarativity and maintainability. Thanks to its relational representation, it was possible to directly represent the input ER-DFD with Prolog facts. Declarativity allowed a straightforward translation of the rigorous rules into code and a quick implementation of a working prototype. Finally, maintainability was a primary concern since the Function Point counting method is continually evolving.},
keywords = {Function Points, Software Metrics, Logic Programming.}
}

@inproceedings{LamMelMil98-LPKR97-IC,
author = {Evelina Lamma AND Paola Mello AND Michela Milano AND Fabrizio Riguzzi},
title = {A System for Abductive Learning of Logic
Programs},
booktitle = {Logic Programming and Knowledge Representation:
Third International Workshop, {LPKR}'97, Port Jefferson, New York,
USA, October 17, 1997. Selected Papers},
editor = {J. Dix and L. M. Pereira and T. Przymusinski},
publisher = {Springer Verlag},
year = 1998,
month = jun,
series = {Lecture Notes on Computer Science},
volume = {1471},
abstract = {We present the system LAP (Learning Abductive Programs)
that is able to learn abductive logic programs from examples and from a
background abductive theory. A new type of induction problem has been
dened as an extension of the Inductive Logic Programming framework.
In the new problem denition, both the background and the target the-
ories are abductive logic programs and abductive derivability is used as
the coverage relation.
LAP is based on the basic top-down ILP algorithm that has been suit-
ably extended. In particular, coverage of examples is tested by using the
abductive proof procedure dened by Kakas and Mancarella [24]. As-
sumptions can be made in order to cover positive examples and to avoid
the coverage of negative ones, and these assumptions can be used as
new training data. LAP can be applied for learning in the presence of
incomplete knowledge and for learning exceptions to classication rules.},
scopus = {2-s2.0-84867834356},
keywords = {Abduction; Negation; Integrity Constraints},
note = {The original publication is available at \url{http://www.springerlink.com}},
doi = {10.1007/BFb0054787},
keywords = {Abduction, Negation, Integrity Constraints},
issn = {0302-9743},
pages = {102--122},
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamMelMil-LPKR97.pdf},
abstract = {We present the system LAP (Learning Abductive Programs)
that is able to learn abductive logic programs from examples and from a
background abductive theory. A new type of induction problem has been
dened as an extension of the Inductive Logic Programming framework.
In the new problem denition, both the background and the target the-
ories are abductive logic programs and abductive derivability is used as
the coverage relation.
LAP is based on the basic top-down ILP algorithm that has been suit-
ably extended. In particular, coverage of examples is tested by using the
abductive proof procedure dened by Kakas and Mancarella [24]. As-
sumptions can be made in order to cover positive examples and to avoid
the coverage of negative ones, and these assumptions can be used as
new training data. LAP can be applied for learning in the presence of
incomplete knowledge and for learning exceptions to classication rules.}
}

@inproceedings{Rig98-ECAI98-IC,
author = {Fabrizio ~Riguzzi},
title = {Integrating Abduction and Induction},
booktitle = {Proceedings of the 13th European Conference on Artificial
Intelligence ({ECAI98}), Brighton, UK, August 23--28 1998},
publisher = {John Wiley and Sons},
year = 1998,
month = aug,
pages = {475--476},
keywords = {Abduction, Negation, Integrity Constraints},
pdf = {http://mcs.unife.it/~friguzzi/Papers/Rig-ECAI98.pdf},
wos = {WOS:000085168300114},
isbn = {0471984310}
}

@inproceedings{KakLamRig98-AIMSA98-IC,
author = { Antonis Kakas and Evelina Lamma and Fabrizio Riguzzi},
title = {Learning Multiple Predicates},
booktitle = {Artificial Intelligence: Methodology, Systems, and Applications:
8th International Conference, {AIMSA}'98, Sozopol,
Bulgaria,  September 21-23, 1998. Proceedings},
series = {Lecture Notes on Artificial Intelligence},
volume = {1480},
note = {The original publication is available at \url{http://www.springerlink.com}},
publisher = {Springer Verlag},
year = 1998,
month = sep,
pages = {303--316},
keywords = {Abduction, Multiple Predicate Learning},
issn = {0302-9743},
isbn = {354064993X},
pdf = {http://mcs.unife.it/~friguzzi/Papers/KakLamRig-AIMSA98.pdf},
doi = {10.1007/BFb0057454},
abstract = {We present an approach for solving some of the problems of
top-down Inductive Logic Programming systems when learning multiple predicates.
The approach is based on an algorithm for learning abductive logic programs.
Abduction is used to generate additional information that is useful
for solving the problem of global inconsistency when learning
multiple predicates.},
scopus = {2s2.084867758281},
wos = {WOS:000083673700025}
}

@inproceedings{LamMelMil97-JCIS97-IC,
author = {Evelina Lamma AND Paola Mello AND Michela Milano AND Fabrizio Riguzzi},
title = {Integrating Induction and Abduction in Logic
Programming},
booktitle = {Proceedings of the Third Joint Conference on Information
Sciences, 1--5 March 1997, Raleigh, North Carolina},
editor = {Paul P. Wang},
pages = {203--206},
year = 1997,
publisher = {Duke University},
volume = {2},
address = {Research Triangle Park, North Carolina, \USA},
month = mar,
keywords = {Abduction, Negation, Integrity Constraints},
isbn = {0-9643456-5-x},
abstract = {We propose an approach for the integration of induc-
tive and abductive reasoning.},
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamMelMil-JCIS97.pdf}
}

@inproceedings{LamMelMil97-GULP97-IC,
author = {Evelina ~Lamma AND Paola ~Mello AND Michela ~Milano AND Fabrizio
~Riguzzi},
title = {An Algorithm for Learning Abductive Rules},
booktitle = {Proceedings of the APPIA-GULP-PRODE 97 Joint Conference on
Declarative Programming, Grado, Italy, 16--19 June 1997},
editor = {Moreno Falaschi and Marisa Navarro and Alberto Policriti},
year = 1997,
month = jun,
pages = {295--305},
publisher = {Dipartimento di Matematica e Informatica, Universit\a di Udine and Gruppo
Ricercatore e Utenti di Logic Programming},
keywords = {Abductive Logic Programming. Inductive Logic Programming},
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamMelMil-GULP97.pdf},
abstract = {We propose an algorithm for learning abductive logic programs from examples.
We consider the Abductive Concept Learning framework, an extension of the Induc-
tive Logic Programming framework in which both the background and the target
theories are abductive logic programs and the coverage of examples is replaced by
abductive coverage. The two main benets of this integration are the increased
expressive power of the background and target theories and the possibility of learn-
ing in presence of incomplete knowledge. We show that the algorithm is able to
learn abductive rules and we present an application of the algorithm to a learning
problem in which the background knowledge is incomplete.}
}

@inproceedings{KakRigu97-ILP97-IC,
author = {Antonis C. Kakas AND Fabrizio Riguzzi},
title = {Learning with Abduction},
booktitle = {Inductive Logic Programming: 7th International Workshop, {ILP}-97 Prague, Czech Republic, September 17-20, 1997 Proceedings},
year = 1997,
series = {Lecture Notes on Artificial Intelligence},
volume = {1297},
note = {The original publication is available at \url{http://www.springerlink.com}},
publisher = {Springer Verlag},
month = sep,
scopus = {2-s2.0-84957894431},
keywords = {Abduction, Negation, Integrity_Constraints},
isbn = {3-540-63514-9},
issn = {0302-9743},
doi = {10.1007/3540635149_47},
pages = {181 -- 188},
pdf = {http://mcs.unife.it/~friguzzi/Papers/KakRigu-ILP97.pdf},
keywords = {Machine Learning, Abduction, Logic Programming, Inductive Logic Programming}
}

@inproceedings{LamMelMil97-AI*IA97-IC,
author = {Evelina ~Lamma AND Paola ~Mello AND Michela ~Milano AND Fabrizio
~Riguzzi},
title = {Introducing Abduction into (Extensional)
Inductive Logic Programming Systems},
booktitle = {{AI*IA} 97: Advances in Artificial Intelligence: 5th Congress of the Italian Association for Artificial Intelligence Rome, Italy, September 17-19, Proceedings},
editor = {M. Lenzerini},
series = {Lecture Notes on Artificial Intelligence},
volume = {1321},
note = {The original publication is available at \url{http://www.springerlink.com}},
publisher = {Springer Verlag},
year = 1997,
keywords = {Abduction, Negation, Integrity_Constraints},
month = sep,
doi = {10.1007/3-540-63576-9_107},
issn = {0302-9743},
isbn = {3-540-63576-9},
pages = {183 -- 194},
pdf = {http://mcs.unife.it/~friguzzi/Papers/LamMelMil-AIIA97.pdf},
abstract = {We present the system LAP (Learning Abductive Programs) that
is able to learn abductive logic programs from examples and from a
background abductive theory. A new type of induction problem has
been dened as an extension of the Inductive Logic Programming framework.
In the new problem denition, both the background and the target theories
are abductive logic programs and abductive derivability is used as
the coverage relation. LAP is based on the basic top-down ILP
algorithm that has been suit- ably extended. In particular,
coverage of examples is tested by using the abductive proof
procedure dened by Kakas and Mancarella [24]. As- sumptions can be
made in order to cover positive examples and to avoid the coverage of
negative ones, and these assumptions can be used as new training data.
LAP can be applied for learning in the presence of incomplete
knowledge and for learning exceptions to classication rules.},
scopus = {2-s2.0-84961356665},
keywords = {Machine learning, Nonmonotonic reasoning}
}


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