Rule-based approaches for representing probabilistic ontology mappings

Calì, Andrea and Lukasiewicz, T. and Predoiu, L. and Stuckenschmidt, H. (2008) Rule-based approaches for representing probabilistic ontology mappings. In: da Costa, P.C.G. and d'Amato, C. and Fanizzi, N. and Laskey, K.B. and Laskey, K.J. and Lukasiewicz, T. and Nickles, M. and Pool, M. (eds.) Uncertainty Reasoning for the Semantic Web I. Lecture Notes in Computer Science 5327. Berlin, Germany: Springer, pp. 66-87. ISBN 9783540897644.

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Official URL: http://dx.doi.org/10.1007/978-3-540-89765-1_5

Abstract

Using mappings between ontologies is a common way of approaching the semantic heterogeneity problem on the Semantic Web. To fit into the landscape of Semantic Web languages, a suitable logic-based representation formalism for mappings is needed, which allows to reason with ontologies and mappings in an integrated manner, and to deal with uncertainty and inconsistencies in automatically created mappings. We analyze the requirements for such a formalism, and propose to use frameworks that integrate description logic ontologies with probabilistic rules. We compare two such frameworks and show the advantages of using the probabilistic extensions of their deterministic counterparts. The two frameworks that we compare are tightly coupled probabilistic dl-programs, which tightly combine the description logics behind OWL DL resp. OWL Lite, disjunctive logic programs under the answer set semantics, and Bayesian probabilities, on the one hand, and generalized Bayesian dl-programs, which tightly combine the DLP-fragment of OWL Lite with Datalog (without negation and equality) based on the semantics of Bayesian networks, on the other hand.

Metadata

Item Type:	Book Section
Additional Information:	ISWC International Workshops, URSW 2005-2007, Revised Selected and Invited Papers
Keyword(s) / Subject(s):	Representing probabilistic ontology mappings, rule languages, Semantic Web, uncertainty, inconsistency, probabilistic description logic programs, description logics, disjunctive logic programs, answer set semantics, Bayesian probabilities, Bayesian description logic programs Datalog, Bayesian networks
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Research Centres and Institutes:	Birkbeck Knowledge Lab
Depositing User:	Administrator
Date Deposited:	14 May 2013 10:34
Last Modified:	09 Aug 2023 12:33
URI:	https://eprints.bbk.ac.uk/id/eprint/6739

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