An Approach to Probabilistic Data Integration for the Semantic Web
Andrea Calì and Thomas Lukasiewicz
Abstract
In previous work, we have introduced probabilistic description logic programs for the Semantic Web, which combine description logics, normal programs under the answer set (resp., well-founded) semantics, and probabilistic uncertainty. In this paper, we continue this line of research. We propose an approach to probabilistic data integration for the Semantic Web that is based on probabilistic description logic programs, where probabilistic uncertainty is used to handle inconsistencies between different data sources. It is inspired by recent works on probabilistic data integration in the database and web community.
Book Title
Proceedings of the 2nd ISWC Workshop on Uncertainty Reasoning for the Semantic Web‚ URSW 2006‚ Athens‚ Georgia‚ USA‚ November 5‚ 2006
Editor
Paulo Cesar G. da Costa and Kathryn B. Laskey and Kenneth J. Laskey and Francis Fung and Michael Pool
Publisher
CEUR−WS.org
Series
CEUR Workshop Proceedings
Volume
218
Year
2006