Query Processing Using Ontologies

Abstract

Recently, the database and AI research communities have paid increased attention to ontologies. The main motivating reason is that ontologies promise solutions for complex problems caused by the lack of a good understanding of the semantics of data in many cases. In particular, ontologies have extensively been used to overcome the interoperability problem during the integration of heterogeneous information sources. Moreover, many efforts have been put into developing ontology based techniques for improving the query answering process in database and information systems.In this paper, we present a new approach for query processing within single (object) relational databases using ontology knowledge. Our goal is to process database queries in a semantically more meaningful way. In fact, our approach shows how an ontology can be effectively exploited to rewrite a user query into another one such that the new query provides more meaningful results satisfying the intention of the user. To this end, we develop a set of transformation rules which rely on semantic information extracted from the ontology associated with the database. In addition, we propose a semantic model and a set of criteria to prove the validity of the transformation results. We also address the necessary mappings between an ontology and its underlying database w.r.t. our framework.KeywordsQuery ProcessingSemantic ModelUser QuerySemantic KnowledgeSemantic RuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.