Parallel mining of OWL 2 EL ontology from linked data

Abstract

The Linked Data is a rich common resource with billions of triples available in thousands of datasets. One of the challenges to integrate, query and reuse the Linked Data is to know about the ontology to which the datasets conform. Although there are many ontologies built manually, there are also many RDF datasets published without any prescribed schema to adhere. In this paper, we propose a parallel approach to generate ontologies from large RDF datasets based on statistical data analysis. We divide the large RDF dataset into blocks and allocate them to parallel hardware, then we obtain the statistical data by SPARQL queries, finally the OWL 2 EL axioms are generated based on statistical data analysis. The evaluations tested on two kinds of DBpedia datasets (Mapping-based Dataset with ontology and Raw Infobox Dataset without ontology) show the effectivity and efficiency of our approach. © 2014 Springer International Publishing Switzerland.