The extension-based inference algorithm for pD

Abstract

In this work, we present a scalable rule-based reasoning algorithm for the OWL pD* language. This algorithm uses partial materialization and a syntactic ontology transformation (the extension-based knowledge model) to provide a fast inference. Because the materialized part of the ontology does not contain assertional data, the time consumed by the process, and the number of inferred triples, remain fixed with varying amounts of assertional data. The algorithm uses database reasoning and a query rewriting technique to handle the remaining inference. The extension-based knowledge model and the database reasoning prevent the expected decreases in query performances, which are the natural result of online reasoning during query time. This work also evaluates the efficiency of the proposed method by conducting experiments using LUBM and UOBM benchmarks.