2014.bib

@incollection{RigBel14-URSWa-BC,
  year = {2014},
  isbn = {978-3-319-13412-3},
  booktitle = {Uncertainty Reasoning for the Semantic Web III},
  series = {Lecture Notes in Computer Science},
  editor = {Bobillo, Fernando and Carvalho, Rommel N. and Costa, Paulo C.G. and d'Amato, Claudia and Fanizzi, Nicola and Laskey, Kathryn B. and Laskey, Kenneth J. and Lukasiewicz, Thomas and Nickles, Matthias and Pool, Michael},
  doi = {10.1007/978-3-319-13413-0_4},
  title = {Learning Probabilistic Description Logics},
  publisher = {Springer International Publishing},
  copyright = {Springer International Publishing},
  author = {Riguzzi, Fabrizio and Bellodi, Elena and Lamma, Evelina and Zese, Riccardo and Cota, Giuseppe},
  pages = {63-78},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/RigBel14-URSWa-BC.pdf},
  language = {English},
  volume = {8816},
  note = {The original publication is available at
\url{http://link.springer.com}}
}
@incollection{RigBel14-URSWb-BC,
  year = {2014},
  isbn = {978-3-319-13412-3},
  booktitle = {Uncertainty Reasoning for the Semantic Web III},
  series = {Lecture Notes in Computer Science},
  editor = {Bobillo, Fernando and Carvalho, Rommel N. and Costa, Paulo C.G. and d'Amato, Claudia and Fanizzi, Nicola and Laskey, Kathryn B. and Laskey, Kenneth J. and Lukasiewicz, Thomas and Nickles, Matthias and Pool, Michael},
  doi = {10.1007/978-3-319-13413-0_5},
  title = {Semantics and Inference for Probabilistic Description Logics},
  publisher = {Springer International Publishing},
  copyright = {Springer International Publishing},
  author = {Zese, Riccardo and Bellodi, Elena and Lamma, Evelina and Riguzzi, Fabrizio and Aguiari, Fabiano},
  pages = {79-99},
  language = {English},
  volume = {8816},
  url = {http://ds.ing.unife.it/~friguzzi/Papers/RigBel14-URSWb-BC.pdf},
  note = {The original publication is available at
\url{http://link.springer.com}}
}
@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},
  copyright = {World Scientific},
  address = {Longon, UK},
  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://ds.ing.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}
}
@article{RigBelZes14-FAI-IJ,
  author = {Riguzzi, Fabrizio  and  Bellodi, Elena  and  Zese, Riccardo},
  title = {A History of Probabilistic Inductive Logic Programming},
  journal = {Frontiers in Robotics and AI},
  volume = {1},
  year = {2014},
  number = {6},
  url = {http://www.frontiersin.org/computational_intelligence/10.3389/frobt.2014.00006/abstract},
  doi = {10.3389/frobt.2014.00006},
  issn = {2296-9144},
  abstract = {The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20?years, with many proposals for languages that combine probability with logic programming. Since the start, the problem of learning probabilistic logic programs has been the focus of much attention. Learning these programs represents a whole subfield of Inductive Logic Programming (ILP). In Probabilistic ILP (PILP), two problems are considered: learning the parameters of a program given the structure (the rules) and learning both the structure and the parameters. Usually, structure learning systems use parameter learning as a subroutine. In this article, we present an overview of PILP and discuss the main results.},
  pages = {1-5},
  keywords = {logic programming, probabilistic programming, inductive logic programming, probabilistic logic
programming, statistical relational learning},
  copyright = {by the authors}
}
@article{RigSwi14-TOCL-IJ,
  author = { Fabrizio Riguzzi and Terrance Swift},
  title = {Terminating Evaluation of Logic Programs with Finite Three-Valued
  Models},
  journal = {ACM Transactions on Computational Logic},
  publisher = {ACM},
  copyright = {ACM},
  volume = {15},
  number = {4},
  year = {2014},
  doi = {10.1145/2629337},
  abstract = {
As evaluation methods for logic programs have become more sophisticated, the classes of programs for which
termination can be guaranteed have expanded. From the perspective of answer set programs that include
function symbols, recent work has identified classes for which grounding routines can terminate either on
the entire program [Calimeri et al. 2008] or on suitable queries [Baselice et al. 2009]. From the perspective
of tabling, it has long been known that a tabling technique called subgoal abstraction provides good termination properties for definite programs [Tamaki and Sato 1986], and this result was recently extended
to stratified programs via the class of bounded term-size programs [Riguzzi and Swift 2013]. In this paper
we provide a formal definition of tabling with subgoal abstraction resulting in the SLG
SA algorithm. Moreover, we discuss a declarative characterization of the queries and programs for which SLG
SA terminates. We
call this class strongly bounded term-size programs and show its equivalence to programs with finite wellfounded models. For normal programs strongly bounded term-size programs strictly includes the finitely
ground programs of [Calimeri et al. 2008]. SLG
SA has an asymptotic complexity on strongly bounded termsize programs equal to the best known and produces a residual program that can be sent to an answer set
programming system. Finally, we describe the implementation of subgoal abstraction within the SLG-WAM
of XSB and provide performance results.},
  keywords = {Logic Programming, Tabled Logic Programming, Termination},
  http = {http://dl.acm.org/authorize?N05388},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/RigSwi14-TOCL.pdf},
  pages = {32:1--32:38},
  month = {September},
  issn = {1529-3785},
  address = {New York, NY, USA}
}
@article{BelLamRig14-ICLP-IJ,
  author = { Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi and Santos Costa, Vitor and Riccardo Zese},
  title = {Lifted Variable Elimination for Probabilistic Logic Programming},
  journal = {Theory and Practice of Logic Programming},
  publisher = {Cambridge University Press},
  copyright = {Cambridge University Press},
  number = {Special issue 4-5 - ICLP 2014},
  volume = {14},
  year = {2014},
  pages = {681-695},
  doi = {10.1017/S1471068414000283},
  pdf = {http://arxiv.org/abs/1405.3218},
  keywords = {Probabilistic Logic Programming, Lifted Inference,
  Variable Elimination, Distribution Semantics, ProbLog,
  Statistical Relational Artificial Intelligence},
  abstract = {Lifted inference has been proposed for various probabilistic logical
  frameworks in order to compute the probability of queries in a time that
  depends on the size of the domains of the random variables rather than the
  number of instances. Even if various authors have underlined its importance
  for probabilistic logic programming (PLP), lifted inference has been applied
  up to now only to relational languages outside of logic programming. In this
  paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE)
  to the problem of computing the probability of queries to probabilistic logic
  programs under the distribution semantics. In particular, we extend the Prolog
  Factor Language (PFL) to include two new types of factors that are needed for
  representing ProbLog programs. These factors take into account the existing
  causal independence relationships among random variables and are managed by
  the extension to variable elimination proposed by Zhang and Poole for dealing
  with convergent variables and heterogeneous factors. Two new operators are
  added to GC-FOVE for treating heterogeneous factors. The resulting algorithm,
  called LP2 for Lifted Probabilistic Logic Programming, has been implemented
  by modifying the PFL implementation of GC-FOVE and tested on three benchmarks
  for lifted inference. A comparison with PITA and ProbLog2 shows the potential
  of the approach.},
  isi = {000343203200019},
  scopus = {84904624147}
}
@article{Rig14-CJ-IJ,
  author = {Fabrizio Riguzzi},
  title = {Speeding Up Inference for Probabilistic Logic Programs},
  journal = { The Computer Journal},
  publisher = {Oxford University Press},
  copyright = {Oxford University Press},
  year = {2014},
  volume = {57},
  number = {3},
  pages = {347-363},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/Rig-CJ13.pdf},
  url = {http://comjnl.oxfordjournals.org/cgi/reprint/bxt096?ijkey=TQc2Z3XTWX2P8ez&keytype=ref},
  doi = {10.1093/comjnl/bxt096},
  abstract = {Probabilistic Logic Programming (PLP) allows to represent domains containing many entities connected by uncertain relations and has many applications in particular in Machine Learning.
PITA is a PLP algorithm for computing the probability of queries that exploits tabling, answer subsumption and Binary Decision Diagrams (BDDs). PITA does not impose any restriction on the programs. Other algorithms, such as PRISM, reduce computation time by imposing 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 do, 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.},
  keywords = {Logic Programming, Probabilistic Logic Programming, Distribution Semantics, Logic Programs with Annotated Disjunctions, PRISM, ProbLog}
}
@article{RigZel14-ML-EB,
  author = {Fabrizio Riguzzi and
               Filip  \v{Z}elezn\'{y} },
  title = {Guest editors' introduction: Special issue on {Inductive Logic Programming (ILP 2012)}},
  journal = {Machine Learning},
  year = {2014},
  doi = {10.1007/s10994-013-5398-8},
  url = {http://link.springer.com/article/10.1007%2Fs10994-013-5398-8},
  volume = {94},
  number = {1-2},
  pages = {1-2},
  copyrigth = {Springer}
}
@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},
  address = {Aachen, Germany},
  issn = {1613-0073},
  url = {http://ceur-ws.org/Vol-1187/paper-08.pdf},
  pages = {46-51},
  copyright = {by the authors}
}

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