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Journal Articles Data and Knowledge Engineering Year : 2015

SWRL Rule-selection Methodology for Ontology Interoperability


Data interoperability represents a great challenge for today’s enterprises. Indeed, they use various information systems, each relying on several different models for data representation. Ontologies and notably ontology matching have been recognized as interesting approaches for solving the data interoperability problem. In this paper, we focus on improving the performance of queries addressed over ontology alignments expressed through SWRL rules. Indeed, when considering the context of executing queries over complex and numerous alignments, the number of SWRL rules highly impacts the query execution time. Moreover, when hybrid or backward-chaining reasoning is applied, the query execution time may grow exponentially. Still, the reasoners involved deliver performant results (in terms of execution time) when applied over reduced and simpler rule sets. Based on this statement, and to address the issue of improving the query execution time, we describe a novel approach that allows, for a given query, to ignore unnecessary rules. The proposed Rule Selector (RS) is a middleware between the considered systems and the reasoner present on the triple store side. Through the benchmarks realized we prove that our approach allows considerably minimizing query execution time.
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hal-01202760 , version 1 (21-09-2015)



Tarcisio Mendes de Farias, Ana Roxin, Christophe Nicolle. SWRL Rule-selection Methodology for Ontology Interoperability. Data and Knowledge Engineering, 2015, ⟨10.1016/j.datak.2015.09.001⟩. ⟨hal-01202760⟩
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