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Rule−Based Approaches for Representing Probabilistic Ontology Mappings

Andrea Calì‚ Thomas Lukasiewicz‚ Livia Predoiu and Heiner Stuckenschmidt

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.

Book Title
Uncertainty Reasoning for the Semantic Web I‚ ISWC International Workshops‚ URSW 2005−2007‚ Revised Selected and Invited Papers
Editor
Paulo Cesar G. da Costa and Claudia d'Amato and Nicola Fanizzi and Kathryn B. Laskey and Kenneth J. Laskey and Thomas Lukasiewicz and Matthias Nickles and Michael Pool
ISBN
978−3−540−89764−4
Pages
66−87
Publisher
Springer
Series
Lecture Notes in Computer Science
Volume
5327
Year
2008