@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{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}
}
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