Rule-Based Approaches for Representing Probabilistic Ontology Mappings.

Journal article

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. © 2008 Springer Berlin Heidelberg.

Authors: Calì, A; Lukasiewicz, T; Predoiu, L; Stuckenschmidt, H

• Publication status: Published
• Publisher: Springer
• Journal: URSW (LNCS Vol.)
• Volume: 5327
• Pages: 66-87
• Publication date: 2008-01-01
• DOI: 10.1007/978-3-540-89765-1_5
• EISSN: 1611-3349
• ISSN: 0302-9743
• Language: English
• Keywords: Bayesian probabilities; Bayesian networks; inconsistency; probabilistic description logic programs; answer set semantics; Bayesian description logic programs; Datalog; description logics; Representing probabilistic ontology mappings; rule languages; Semantic Web; disjunctive logic programs; uncertainty