Rule-Based Approaches for Representing Probabilistic Ontology Mappings

  • Authors:

  • Andrea Calì;Thomas Lukasiewicz;Livia Predoiu;Heiner Stuckenschmidt

  • Affiliations:

  • Oxford-Man Institute of Quantitative Finance, University of Oxford, Oxford, UK OX1 4EH and Computing Laboratory, University of Oxford, Oxford, UK OX1 3QD;Computing Laboratory, University of Oxford, Oxford, UK OX1 3QD;Institut für Informatik, Universität Mannheim, Mannheim, Germany 68159;Institut für Informatik, Universität Mannheim, Mannheim, Germany 68159

  • Venue:
  • Uncertainty Reasoning for the Semantic Web I
  • Year:
  • 2008

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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.