Query Evaluation and Optimization in the Semantic Web

Fifth Meeting of IUFRO Working Party 7.03.04 – Diseases and insects in forest nurseries

Edna Ruckhaus ; Eduardo Ruiz ; María-Esther Vidal

Others  :  http://CEUR-WS.org/Vol-196/alpsws2006-paper2.pdf
PID  :  453

来源: CEUR

【 摘 要 】

We address the problem of answering Web ontology queries efficiently. An ontology is formalized as a Deductive Ontology Base (DOB), a deductive database that comprises the ontology’s inference axioms and facts, and we present a cost-based query optimization technique for DOB. A hybrid cost model is proposed to estimate the cost and cardinality of basic and inferred facts. Cardinality and cost of inferred facts are estimated using an adaptive sampling technique, while techniques of traditional relational cost models are used for estimating the cost of basic facts and conjunctive ontology queries. Finally, we implement a dynamic-programming optimization algorithm to identify query evaluation plans that minimize the number of intermediate inferred facts. We modeled a subset of the Web ontology language OWL Lite as a DOB, and performed an experimental study to analyze the predictive capacity of our cost model and the benefits of the query optimization technique. Our study has been conducted over synthetic and real-world OWL ontologies, and shows that the techniques are accurate and improve query performance.