Query Evaluation and Optimization in the Semantic Web

dc.creatorRuckhaus, Edna
dc.creatorRuiz, Eduardo
dc.creatorVidal, Maria-Esther
dc.date2007-11-13
dc.date.accessioned2026-07-07T08:42:50Z
dc.date.available2026-07-07T08:42:50Z
dc.descriptionWe 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. A cost-based query optimization technique for DOB is presented. 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. To appear in Theory and Practice of Logic Programming (TPLP).
dc.description18 pages, 8 figures, 7 tables. Presented at the ALPSWS2006 First International Workshop on Applications of Logic Programming in the Semantic Web and Semantic Web Services where it got a "Best Paper Award". To appear in Theory and Practice of Logic Programming (TPLP)
dc.identifierhttps://arxiv.org/abs/0711.2087
dc.identifierhttp://arxiv.org/abs/0711.2087
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/142106
dc.subjectDatabases
dc.subjectLogic in Computer Science
dc.subjectF.4.1; H.2.3; I.2.4
dc.titleQuery Evaluation and Optimization in the Semantic Web
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