Improving the Performance of a SPARQL Component for Semantic Web Applications

Abstract. The vision of the Semantic Web is to make use of semantic representations on the largest possible scale- the Web. Large knowl-edge bases such as DBpedia, OpenCyc, GovTrack are emerging and freely available as Linked Data and SPARQL endpoints. Exploring and analysing such knowledge bases is a significant hurdle for Semantic Web research and practice. As one possible direction for tackling this prob-lem, we present an approach for obtaining complex class expressions from objects in knowledge bases by using Machine Learning techniques. We describe in detail how they leverage existing techniques to achieve scala-bility on large knowledge bases available as SPARQL endpoints or Linked Data. The algorithms are made available in the open source DL-Learner project and we present several real-life scenarios in which they can be used by Semantic Web applications. Because of the wide usage of the method in several well-known tools, we optimized and benchmarked the existing algorithms and show that we achieve an approximately 3-fold increase in speed, in addition to a more robust implementation.