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On Building an Index Advisor for Semantic Web Queries
Lubomir Stanchev, Grant Weddell
147 - 157
10.3233/978-1-60750-535-8-147
Frontiers in Artificial Intelligence and Applications
Volume 209: Formal Ontology in Information Systems
Abstract

Current optimization techniques for answering queries over Semantic Web data use realization to precalculate the individuals associated with every concept in the given ontology. However, this technique does not take into account the type of queries, written for example in nRQL or SPARQL-DL, that will arrive at the system. In this paper we propose how this additional knowledge can be used to create query-specific indices. We include experimental results that show how our approach can be used to improve the performance of the Pellet query engine for the popular LUBM benchmark.

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