@inproceedings{CotZes15-AIIADC-IW,
title = {Learning Probabilistic Ontologies with Distributed Parameter Learning },
author = {Giuseppe Cota and Riccardo Zese and Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi},
pages = {7--12},
pdf = {http://ceur-ws.org/Vol-1485/paper2.pdf},
booktitle = {Proceedings of the Doctoral Consortium (DC)
co-located with the 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015)},
year = 2015,
editor = {Elena Bellodi and Alessio Bonfietti},
volume = 1485,
series = {CEUR Workshop Proceedings},
address = {Aachen, Germany},
issn = {1613-0073},
venue = {Ferrara, Italy},
eventdate = {2015-09-23/24},
publisher = {Sun {SITE} Central Europe},
copyright = {by the authors},
abstract = {
We consider the problem of learning both the structure and
the parameters of Probabilistic Description Logics under DISPONTE.
DISPONTE ("DIstribution Semantics for Probabilistic ONTologiEs")
adapts the distribution semantics for Probabilistic Logic Programming
to Description Logics. The system LEAP for "LEArning Probabilistic
description logics" learns both the structure and the parameters of
DISPONTE knowledge bases (KBs) by exploiting the algorithms CELOE
and EDGE. The former stands for "Class Expression Learning for Ontology
Engineering" and it is used to generate good candidate axioms
to add to the KB, while the latter learns the probabilistic parameters
and evaluates the KB. EDGE for "Em over bDds for description loGics
paramEter learning" is an algorithm for learning the parameters of probabilistic
ontologies from data. In order to contain the computational cost,
a distributed version of EDGE called EDGEMR was developed. EDGEMR
exploits the MapReduce (MR) strategy by means of the Message Passing
Interface. In this paper we propose the system LEAPMR. It is a
re-engineered version of LEAP which is able to use distributed parallel
parameter learning algorithms such as EDGEMR.
},
keywords = {Probabilistic Description Logics, Structure Learning,
Parameter Learning, MapReduce, Message Passing Interface.
}
}
@inproceedings{ZesBel15-AIIADC-IW,
title = {Tableau Reasoners for Probabilistic Ontologies Exploiting Logic Programming Techniques},
author = {Riccardo Zese and Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma},
pages = {1--6},
pdf = {http://ceur-ws.org/Vol-1485/paper1.pdf},
booktitle = {Proceedings of the Doctoral Consortium (DC)
co-located with the 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015)},
year = 2015,
editor = {Elena Bellodi and Alessio Bonfietti},
volume = 1485,
series = {CEUR Workshop Proceedings},
address = {Aachen, Germany},
issn = {1613-0073},
venue = {Ferrara, Italy},
eventdate = {2015-09-23/24},
publisher = {Sun {SITE} Central Europe},
copyright = {by the authors},
abstract = {The adoption of Description Logics for modeling real world
domains within the Semantic Web is exponentially increased in the last
years, also due to the availability of a large number of reasoning algorithms.
Most of them exploit the tableau algorithm which has to manage
non-determinism, a feature that is not easy to handle using procedural
languages such as Java or C++. Reasoning on real world domains also
requires the capability of managing probabilistic and uncertain information.
We thus present TRILL, for "Tableau Reasoner for descrIption
Logics in proLog" and TRILLP
, for "TRILL powered by Pinpointing
formulas", which implement the tableau algorithm and return the probability
of queries. TRILLP
, instead of the set of explanations for a query,
computes a Boolean formula representing them, speeding up the computation.
},
keywords = {Distribution Semantics, Probabilistic Semantic Web,
Logic Programming, Description Logics},
scopus = {2-s2.0-85009168558}
}
@inproceedings{CotZesBel15-ECMLDC-IW,
year = {2015},
booktitle = {Doctoral Consortium of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
editor = {Jaakko Hollmen and Panagiotis Papapetrou },
title = {Structure Learning with Distributed Parameter
Learning for Probabilistic Ontologies},
author = {Giuseppe Cota and Riccardo Zese and Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi},
pages = {75--84},
copyright = {by the authors},
url = {http://urn.fi/URN:ISBN:978-952-60-6443-7},
pdf = {https://aaltodoc.aalto.fi/bitstream/handle/123456789/18224/isbn9789526064437.pdf#page=79},
isbn = {978-952-60-6443-7},
issn = {1799-490X},
issn = {1799-4896},
abstract = {We consider the problem of learning both the structure and
the parameters of Probabilistic Description Logics under DISPONTE.
DISPONTE ("DIstribution Semantics for Probabilistic ONTologiEs")
adapts the distribution semantics for Probabilistic Logic Programming
to Description Logics. The system LEAP for "LEArning Probabilistic
description logics" learns both the structure and the parameters of
DISPONTE knowledge bases (KBs) by exploiting the algorithms CELOE
and EDGE. The former stands for "Class Expression Learning for Ontology
Engineering" and it is used to generate good candidate axioms
to add to the KB, while the latter learns the probabilistic parameters
and evaluates the KB. EDGE for "Em over bDds for description loGics
paramEter learning" is an algorithm for learning the parameters of probabilistic
ontologies from data. In order to contain the computational cost,
a distributed version of EDGE called EDGEMR was developed. EDGEMR
exploits the MapReduce (MR) strategy by means of the Message Passing
Interface. In this paper we propose the system LEAPMR. It is a
re-engineered version of LEAP which is able to use distributed parallel
parameter learning algorithms such as EDGEMR.},
keywords = {Probabilistic Description Logics, Structure Learning,
Parameter Learning, MapReduce, Message Passing Interface}
}
@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},
address = {Aachen, Germany},
copyright = {by the authors},
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{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{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},
wos = {WOS:000442637804062},
scopus = {2-s2.0-84949759647}
}
@inproceedings{ZesBel15-OntoLP-IW,
author = {Riccardo Zese and Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi},
title = {Logic Programming Techniques for Reasoning with Probabilistic Ontologies},
booktitle = {
Joint Ontology Workshops 2015, JOWO 2015 - Episode 1: The Argentine Winter of
Ontology; Buenos Aires; Argentina;
25 July 2015 through 27 July 2015},
editor = {Odile Papini and
Salem Benferhat and
Laurent Garcia and
Marie-Laure Mugnier and
Eduardo Fermé and
Thomas Meyer and
Renata Wassermann and
Torsten Hahmann and
Ken Baclawski and
Adila Krisnadhi and
Pavel Klinov and
Stefano Borgo and
Oliver Kutz and
Daniele Porello},
year = {2015},
pdf = {http://ceur-ws.org/Vol-1517/JOWO-15_ontolp_paper_3.pdf},
volume = 1517,
series = {CEUR Workshop Proceedings},
address = {Aachen, Germany},
issn = {1613-0073},
venue = {Buenos Aires, Argentine},
eventdate = {2015-07-25/27},
publisher = {Sun {SITE} Central Europe},
keywords = {Description Logics, Tableau, Prolog, Semantic Web, Pinpoiting Formula},
abstract = {The increasing popularity of the Semantic Web drove to a widespread adoption of Description Logics (DLs) for modeling real world domains.
To help the diffusion of DLs a large number of reasoning algorithms have been developed. Usually these algorithms
are implemented in procedural languages such as Java or C++. Most of the reasoners exploit the tableau algorithm which has to manage non-determinism,
a feature that is hard to handle using such languages. Reasoning on real world domains also requires the capability of managing probabilistic and uncertain information.
We thus present TRILL for ``Tableau Reasoner for descrIption Logics in proLog'' that implements a tableau algorithm and is able to return explanations for the queries and the corresponding probability,
and TRILL$^P$ for ``TRILL powered by Pinpointing formulas'' which is able to compute a Boolean formula representing the set of explanations for the query. This approach can speed up
the process of computing the probability.
Prolog non-determinism is used for easily handling the tableau's non-deterministic expansion rules.},
copyright = {CC0 \url{https://creativecommons.org/publicdomain/zero/1.0/}}
}
@article{DiMBelRig15-ML-IJ,
author = {Di Mauro, Nicola and Elena Bellodi and Fabrizio Riguzzi},
title = {Bandit-Based {Monte-Carlo} Structure Learning of
Probabilistic Logic Programs},
journal = {Machine Learning},
publisher = {Springer International Publishing},
copyright = {Springer International Publishing},
year = {2015},
volume = {100},
number = {1},
pages = {127-156},
month = {July},
doi = {10.1007/s10994-015-5510-3},
url = {http://ml.unife.it/wp-content/uploads/Papers/DiMBelRig-ML15.pdf},
keywords = {probabilistic inductive logic programming, statistical relational learning, structure learning, distribution semantics, logic programs with annotated disjunction},
abstract = {Probabilistic Logic Programming can be used to model domains with complex and uncertain relationships among entities. While the problem of learning the
parameters of such programs has been considered by various authors, the problem
of learning the structure is yet to be explored in depth. In this work we present an
approximate search method based on a one-player game approach, called LEMUR. It
sees the problem of learning the structure of a probabilistic logic program as a multiarmed bandit problem, relying on the Monte-Carlo tree search UCT algorithm that
combines the precision of tree search with the generality of random sampling. LEMUR
works by modifying the UCT algorithm in a fashion similar to FUSE, that considers a
finite unknown horizon and deals with the problem of having a huge branching factor.
The proposed system has been tested on various real-world datasets and has shown
good performance with respect to other state of the art statistical relational learning
approaches in terms of classification abilities.},
note = {The original publication is available at
\url{http://link.springer.com}}
}
@article{RigBelLamZes15-SW-IJ,
author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese},
title = {Probabilistic Description Logics under the Distribution Semantics},
journal = {Semantic Web - Interoperability, Usability, Applicability},
volume = {6},
number = {5},
pages = {447-501},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/RigBelLamZes-SW14.pdf},
year = {2015},
doi = {10.3233/SW-140154},
abstract = {
Representing uncertain information is crucial for modeling real world domains. In this paper we present a technique for the integration of probabilistic information in Description Logics (DLs) that is based on the distribution semantics for probabilistic logic programs. In the resulting approach, that we called DISPONTE, the axioms of a probabilistic knowledge base
(KB) can be annotated with a real number between 0 and 1. A probabilistic knowledge base then defines a probability
distribution over regular KBs called worlds and the probability of a given query can be obtained from the joint distribution of the worlds and the query by marginalization.
We present the algorithm BUNDLE for computing the probability of queries from DISPONTE KBs. The algorithm 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 experimentation of BUNDLE shows that it can handle probabilistic KBs of realistic size.
},
keywords = { Probabilistic Ontologies, Probabilistic Description Logics, OWL, Probabilistic Logic Programming, Distribution Semantics}
}
@article{BelRig15-TPLP-IJ,
author = {Elena Bellodi and Fabrizio Riguzzi},
title = {Structure Learning of Probabilistic Logic Programs by Searching the Clause Space},
journal = {Theory and Practice of Logic Programming},
publisher = {Cambridge University Press},
copyright = {Cambridge University Press},
year = {2015},
volume = {15},
number = {2},
pages = {169-212},
pdf = {http://arxiv.org/abs/1309.2080},
url = {http://journals.cambridge.org/abstract_S1471068413000689},
doi = {10.1017/S1471068413000689},
keywords = {probabilistic inductive logic programming, statistical relational learning, structure learning, distribution semantics, logic programs with annotated disjunction, CP-logic},
abstract = {Learning probabilistic logic programming languages is receiving an increasing attention,
and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog
and EMBLEM) or both structure and parameters (SEM-CP-logic and SLIPCASE) of these
languages. In this paper we present the algorithm SLIPCOVER for "Structure LearnIng
of Probabilistic logic programs by searChing OVER the clause space." It performs a beam
search in the space of probabilistic clauses and a greedy search in the space of theories
using the log likelihood of the data as the guiding heuristics. To estimate the log likelihood,
SLIPCOVER performs Expectation Maximization with EMBLEM. The algorithm has been
tested on five real world datasets and compared with SLIPCASE, SEM-CP-logic, Aleph and
two algorithms for learning Markov Logic Networks (Learning using Structural Motifs (LSM)
and ALEPH++ExactL1). SLIPCOVER achieves higher areas under the precision-recall and
receiver operating characteristic curves in most cases.}
}
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