@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://ml.unife.it/wp-content/uploads/Papers/BelRigLam-CILC10.pdf},
copyright = {by the authors}
}
@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}
}
@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://ml.unife.it/wp-content/uploads/Papers/RigBelLam12-CILC12.pdf},
pdf = {http://ceur-ws.org/Vol-857/paper_f16.pdf},
pages = {221-235}
}
@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://ml.unife.it/wp-content/uploads/Papers/ZesBelLamRig-CILC13.pdf},
url = {http://ceur-ws.org/Vol-1068/paper-l02.pdf},
copyright = {by the authors}
}
@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{AzzBelRig22-abdPASP-NC,
title = {Abduction in (Probabilistic) Answer Set Programming},
author = {Azzolini, Damiano and Bellodi, Elena and Riguzzi, Fabrizio},
year = {2022},
editor = {Roberta Calegari and Giovanni Ciatto and Andrea Omicini},
booktitle = {Proceedings of the 37th Italian Conference on Computational Logic (CILC2022)},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
address = {Aachen, Germany},
issn = {1613-0073},
venue = {Bologna, Italy},
volume = {3204},
pages = {90--103},
pdf = {http://ceur-ws.org/Vol-3204/paper_9.pdf}
}
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