natconferences.bib

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@inproceedings{AlbLamRig17-CILC-NC,
  author = {Marco Alberti and Evelina Lamma and Fabrizio Riguzzi and Riccardo Zese},
  title = {Iterated Fixpoint Well-founded Semantics for Hybrid Knowledge Bases},
  booktitle = {Joint Proceedings of the 18th Italian Conference on Theoretical Computer Science and
the 32nd Italian Conference on Computational Logic},
  eventdate = {26-28 September 2017},
  venue = {Naples, Italy},
  editor = {{Dario Della Monica} and Aniello Murano and Sasha Rubin and Luigi Sauro},
  year = {2017},
  series = {CEUR Workshop Proceedings},
  address = {Aachen, Germany},
  issn = {1613-0073},
  publisher = {Sun {SITE} Central Europe},
  pages = {248-261},
  pdf = {http://ceur-ws.org/Vol-1949/CILCpaper01.pdf},
  volume = 1949,
  abstract = {
MKNF-based Hybrid Knowledge Bases (HKBs) integrate Logic Programming (LP) and
Description Logics (DLs) offering the combined expressiveness of the two formalisms.
In particular, HKB allow to make different closure assumptions for different predicates.
HKBs have been given a well-founded semantics in terms of an alternate fixpoint.
In this paper we provide an alternative definition of the semantics using an
iterated fixpoint. In this way the computation of the well-founded model proceeds
uniformly bottom-up, making the semantics easier to understand, to reason with and to automate.
We also present slightly different but equivalent versions of our definition.
We then discuss the relationships of HKBs with other formalisms.
The results show that overall HKBs seem to be those that more tightly integrate LP and DL,
even if there exist incomparable languages such as the recent FO(ID) formalism.},
  keywords = {Hybrid Knowledge Bases, MKNF, Well-foudned semantics, Description Logics}
}
@inproceedings{GavLamRig15-CILC15-NC,
  title = {Abductive Logic Programming for {Datalog+-} Ontologies},
  author = {Marco Gavanelli and Evelina Lamma and  Fabrizio Riguzzi and Elena Bellodi and Riccardo Zese and Giuseppe Cota},
  booktitle = {Proceedings of the 30th Italian Conference on Computational Logic ({CILC2015}),
Genova, Italy, 1-3 July 2015},
  editor = {Davide Ancona and
Marco Maratea and
Viviana Mascardi},
  year = {2015},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  issn = {1613-0073},
  address = {Aachen, Germany},
  copyright = {by the authors},
  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.
The SCIFF language smoothly supports the integration of rules, expressed in a Logic Programming language, with Datalog+- ontologies,  mapped  into SCIFF (forward) integrity constraints.
The main advantage is that this integration is achieved within a single language, grounded on abduction in computational logic.
},
  keywords = { Abductive Logic Programming, Description Logics,  Semantic Web},
  number = {1459},
  pages = {128-143},
  url = {http://ceur-ws.org/Vol-1459/paper21.pdf}
}
@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},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/ZesBelLamRig-CILC13.pdf},
  url = {http://ceur-ws.org/Vol-1068/paper-l02.pdf},
  copyright = {by the authors}
}
@inproceedings{RigBelLam12-CILC12-NC,
  author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma},
  title = {Probabilistic Ontologies in {Datalog+/-}},
  booktitle = {Proceedings of the 27th Italian Conference on Computational Logic ({CILC2012}),
Roma, Italy, 6-7 June 2012},
  year = {2012},
  abstract = {In logic programming the distribution semantics is one of the most popular approaches for dealing with uncertain information. In this paper
we apply the distribution semantics to the  Datalog+/- language that is grounded in logic programming and allows tractable ontology querying. In the resulting semantics, called DISPONTE, 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.
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},
  volume = {857},
  address = {Aachen, Germany},
  url = {http://ds.ing.unife.it/~friguzzi/Papers/RigBelLam12-CILC12.pdf},
  pdf = {http://ceur-ws.org/Vol-857/paper_f16.pdf},
  pages = {221-235}
}
@inproceedings{Rig12-CILC12-NC,
  author = {Fabrizio Riguzzi},
  title = {Optimizing Inference for Probabilistic Logic Programs Exploiting Independence and Exclusiveness},
  booktitle = {Proceedings of the 27th Italian Conference on Computational Logic ({CILC2012}),
Roma, Italy, 6-7 June 2012},
  year = {2012},
  abstract = {Probabilistic Logic Programming (PLP) is gaining popularity due to its many applications in particular in Machine Learning. An important problem in PLP is how to compute the probability of queries.
PITA is an algorithm for solving such a problem that exploits tabling, answer subsumption and Binary Decision Diagrams (BDDs). PITA does not impose any restriction on the programs. Other algorithms, such as PRISM, achieve a higher speed by imposing two restrictions on the program, namely that subgoals are independent and that clause bodies are mutually exclusive. Another assumption that simplifies inference is that clause bodies are independent. In this paper we  present the algorithms PITA(IND,IND) and PITA(OPT). PITA(IND,IND) assumes that subgoals and clause bodies are independent. PITA(OPT) instead first checks whether these assumptions hold for subprograms and subgoals: if they hold, PITA(OPT) uses a simplified calculation, otherwise it resorts to BDDs. Experiments on a number of benchmark datasets show that PITA(IND,IND) is the fastest on datasets respecting the assumptions while PITA(OPT) is a good option when nothing is known about a dataset.
},
  copyright = {by the authors},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  issn = {1613-0073},
  address = {Aachen, Germany},
  url = {http://ds.ing.unife.it/~friguzzi/Papers/Rig12-CILC12.pdf},
  pdf = {http://ceur-ws.org/Vol-857/paper_f15.pdf},
  volume = {857},
  pages = {206-220}
}
@inproceedings{BelRig11-CILC11-NC,
  author = {Elena Bellodi and Fabrizio Riguzzi},
  title = {{EM} over Binary Decision Diagrams for Probabilistic Logic Programs},
  booktitle = {Proceedings of the 26th Italian Conference on Computational Logic ({CILC2011}), Pescara, Italy, 31 August 31-2 September, 2011},
  year = {2011},
  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 techniques - 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 usage.
},
  url = {http://ds.ing.unife.it/~friguzzi/Papers/BelRig-CILC11.pdf},
  copyright = {by the authors},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  issn = {1613-0073},
  address = {Aachen, \Germany},
  volume = {810},
  pdf = {http://ceur-ws.org/Vol-810/paper-l14.pdf},
  pages = {229-243}
}
@inproceedings{Rig11-CILC11-NC,
  author = {Fabrizio Riguzzi},
  title = {{MCINTYRE}: A {Monte Carlo} Algorithm for Probabilistic Logic Programming},
  booktitle = {Proceedings of the 26th Italian Conference on Computational Logic ({CILC2011}),
Pescara, Italy, 31 August-2 September, 2011},
  editor = {Fabio Fioravanti},
  year = {2011},
  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 \texttt{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. In the transformation, auxiliary atoms are added to the body of rules for performing sampling and checking for the consistency of the sample. The current sample is stored in the internal database of the Yap Prolog engine.
The resulting algorithm, 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 \texttt{cplint} and with the  exact inference  of the PITA system. The results show  that MCINTYRE is faster than the other Monte Carlo algorithms.
},
  keywords = {Probabilistic Logic Programming,
Monte Carlo Methods,
Logic Programs with Annotated Disjunctions,
ProbLog},
  url = {http://ds.ing.unife.it/~friguzzi/Papers/Rig-CILC11.pdf},
  copyright = {by the author},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  issn = {1613-0073},
  address = {Aachen, \Germany},
  volume = {810},
  pdf = {http://ceur-ws.org/Vol-810/paper-l02.pdf},
  pages = {25--39}
}
@inproceedings{RigSwi10-CILC10-NC,
  author = {Fabrizio Riguzzi and Terrance Swift},
  title = {An Extended Semantics for Logic Programs with Annotated Disjunctions and its Efficient Implementation},
  booktitle = {Proceedings of the 25th Italian Conference on Computational Logic ({CILC2010}),
Rende, Italy, July 7-9, 2010.},
  year = {2010},
  abstract = { Logic Programming with Annotated Disjunctions (LPADs) is  a formalism for modeling probabilistic information that has recently received  increased attention.
The LPAD semantics, while being simple and clear, suffers from the requirement of having function free-programs, which is a strong limitation.
In this paper we present an extension of the semantics that removes this restriction and allows us to write programs modeling infinite domains, such as Hidden Markov Models.
We show that the semantics is well-defined for a large class of programs.
Moreover, we present the algorithm ``Probabilistic 
Inference with Tabling and Answer subsumption'' (PITA) for
computing the probability of queries to programs according to the extended semantics. Tabling and answer subsumption not only ensure the correctness of the algorithm with respect to the semantics but also make it very efficient on programs without function symbols.
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 both type of domains.},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  issn = {1613-0073},
  volume = {598},
  address = {Aachen, \Germany},
  pdf = {http://ceur-ws.org/Vol-598/paper16.pdf},
  copyright = {by the authors}
}
@inproceedings{BelRigLam10-CILC10-NC,
  author = {Elena Bellodi and Fabrizio Riguzzi and  Evelina Lamma},
  title = {Probabilistic Logic-based Process Mining},
  booktitle = {Proceedings of the 25th Italian Conference on Computational Logic ({CILC2010}),
Rende, Italy, July 7-9, 2010.},
  year = {2010},
  abstract = {
The management of business processes has recently received much attention, since it can support significant efficiency improvements in organizations. One of the most interesting problems is the description of a process model in a language, also equipped with an operational support, that allows  checking the compliance of a process execution (trace) to the model. Another problem of interest is the induction of these models from data.
In this paper, we present a logic-based approach for the induction of process models that are expressed by means of a probabilistic logic.
The approach first uses the DPML 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 for each formula are tuned using the Alchemy system.
The resulting theory allows to perform probabilistic classification 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.},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  issn = {1613-0073},
  address = {Aachen, \Germany},
  volume = {598},
  pdf = {http://ceur-ws.org/Vol-598/paper17.pdf},
  url = {http://ds.ing.unife.it/~friguzzi/Papers/BelRigLam-CILC10.pdf},
  copyright = {by the authors}
}
@inproceedings{CheMelMon-CILC08-NC,
  author = {
Federico Chesani and Paola Mello and Marco Montali and Fabrizio
Riguzzi and  Maurizio
Sebastianis and Sergio Storari},
  title = {Compliance Checking of Execution Traces to Business Rules:
an Approach based on Logic Programming},
  booktitle = {Atti del 23esimo Convegno Italiano
di Logica Computazionale, Perugia, Italia, 10-12 luglio 2008},
  year = {2008},
  abstract = {
Complex and flexible business processes are critical not only
because they are difficult to handle, but also because they often
tend to be less intelligible. Monitoring and verifying complex and
flexible processes becomes therefore a fundamental requirement. We
propose a framework for performing compliance checking of process
execution traces w.r.t.~expressive reactive business rules, tailored
to the MXML meta-model. Rules are mapped to (extensions of) Logic
Programming, to the aim of providing both monitoring and
a-posteriori verification capabilities. We show how different rule
templates, inspired by the ConDec language, can be easily specified
and then customized in the context of a real industrial case study.
We finally describe how the proposed language and its underlying
a-posteriori reasoning technique have been concretely implemented as
a ProM analysis plug-in.},
  editor = {Andrea Formisano},
  publisher = {Dipartimento di Matematica e Informatica, Universit\`a di Perugia},
  keywords = {Business Process Management, Logic Programming},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/CheMelMon08-CILC.pdf}
}

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