@inproceedings{BelLamRigAlb11-URSW11-IW,
author = {Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi and Simone Albani },
editor = {Fernando Bobillo and
Rommel Carvalho and
da Costa, Paulo C. G. and
d'Amato, Claudia and
Nicola Fanizzi and
Laskey, Kathryn B. and
Laskey, Kenneth J. and
Thomas Lukasiewicz and
Trevor Martin and
Matthias Nickles and
Michael Pool},
title = {A Distribution Semantics for Probabilistic Ontologies},
booktitle = {Proceedings ot the 7th International Workshop on Uncertainty Reasoning for the Semantic Web, Bonn, Germany, 23 October, 2011 },
year = {2011},
url = {http://ml.unife.it/wp-content/uploads/Papers/BelLamRigAlb-URSW11.pdf},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
issn = {1613-0073},
address = {Aachen, \Germany},
volume = {778},
pages = {75-86},
pdf = {http://ceur-ws.org/Vol-778/paper7.pdf},
abstract = {We present DISPONTE, a semantics for probabilistic ontologies that is based on the distribution semantics for probabilistic logic programs. In DISPONTE each axiom of a probabilistic ontology is annotated with a probability. The probabilistic theory defines thus a distribution over normal theories (called worlds) obtained by including an axiom in a world with a probability given by the annotation. The probability of a query is computed from this distribution with marginalization.
We also present the system BUNDLE for reasoning over probabilistic OWL DL ontologies according to the DISPONTE semantics. BUNDLE is based on Pellet and uses its capability of returning explanations for a query. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed.}
}
@inproceedings{BelRig11-MCP11-IW,
author = {Elena Bellodi and Fabrizio Riguzzi},
title = {An {Expectation Maximization} Algorithm for Probabilistic Logic Programs},
booktitle = {Proceedings of the Workshop on Mining Complex Patterns ({MCP2011}), 17 September 2011},
address = {Palermo, Italy},
editor = {Appice, Annalisa and Ceci, Michelangelo and Loglisci, Corrado and Manco, Giuseppe},
year = {2011},
month = sep,
pages = {26-37},
abstract = {
Recently much work in Machine Learning has concentrated on representation languages able to combine aspects of logic and probability, leading to the birth of a whole field called Statistical Relational Learning.
In this paper we present a technique for parameter learning targeted to a family of formalisms where uncertainty is represented using Logic Programming tools - the so-called Probabilistic Logic Programs such as ICL, PRISM, ProbLog and LPAD.
Since their equivalent Bayesian networks contain hidden variables, an EM algorithm is adopted.
In order to speed the computation, expectations are computed directly on the Binary Decision Diagrams that are built for inference.
The resulting system, called EMBLEM for ``EM over BDDs for probabilistic Logic programs Efficient Mining'', has been applied to a number of datasets and showed good performances both in terms of speed and memory.
},
url = {http://ml.unife.it/wp-content/uploads/Papers/BelRig-MCP11.pdf},
copyright = {by the authors},
keywords = { Statistical Relational Learning, Probabilistic Logic Programs, Logic Programs with Annotated Disjunction, Expectation Maximization, Binary Decision Diagrams}
}
@inproceedings{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://ml.unife.it/wp-content/uploads/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}
}
@techreport{BelRig11-TR,
author = {Elena Bellodi and Fabrizio Riguzzi},
title = { {EM} over Binary Decision Diagrams for Probabilistic Logic Programs},
year = {2011},
institution = {Dipartimento di Ingegneria, Universit\`a di Ferrara, Italy},
number = {CS-2011-01},
url = {http://ml.unife.it/wp-content/uploads/Papers/CS-2011-01.pdf}
}
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