An Approach to Probabilistic Data Integration for the Semantic Web

Abstract

Probabilistic description logic programs are a powerful tool for knowledge representation in the Semantic Web, which combine description logics, normal programs under the answer set or well-founded semantics, and probabilistic uncertainty. The task of data integration amounts to providing the user with access to a set of heterogeneous data sources in the same fashion as when querying a single database, that is, through a global schema, which is a common representation of all the underlying data sources. In this paper, we make use of probabilistic description logic programs to model expressive data integration systems for the Semantic Web, where constraints are expressed both over the data sources and the global schema. We describe different types of probabilistic data integration, which aim especially at applications in the Semantic Web.