Large-scale incremental OWL/RDFS reasoning over fuzzy RDF data

Ontological RDF data are extracted from multiple sources on the web through mapping and alignment for various purposes, but extracting and reasoning about ontologies from different sources causes information ambiguity and uncertainty. A reasonable solution to this problem is to annotate extracted ontology data with truth values to determine the reliability of information. However, the recent growth in data has brought forth difficulties in ascertaining the credibility of numerous ontologies during OWL/RDFS reasoning. In this paper, we present a distributed and incremental reasoning approach for RDF data with uncertainty. We focused on RDFS and OWL pD* semantics and developed methods for incremental OWL reasoning with uncertainty. We also introduced parallel algorithms that resolve the scalable reasoning problem. To evaluate the efficiency of the proposed system, we conducted OWL/RDFS reasoning over fuzzy LUBM3000 and achieved a performance three times higher than that achieved with the fastest reasoning system.