Optimizing the Computation of a Possibilistic Heuristic to Test OWL SubClassOf Axioms Against RDF Data

The growth of the semantic Web requires tools to manage data, make them available to humans and for a wide range of applications. In particular, tools dedicated to ontology management are a keystone for semantic Web applications. In this paper we consider a possibilistic framework and an evolutionary approach for ontology enrichment with OWL axioms. The assessment of candidate OWL axioms against an RDF knowledge graph requires a high computational cost, especially in terms of computation time (CPU), which may limit the applicability of the framework. To answer this problem, our contribution presented in this paper consists of (i) a multi-threading system to parallelize axiom assessment, (ii) a heuristic to avoid redundant computation and (iii) an optimization for SPARQL query chunking relying on an extension of the SPARQL 1.1 Federated Query standard. The results of a comparative evaluation show that our proposal significantly outperforms the original algorithm, enabling a significant reduction in computation time.