Towards a better insight of RDF triples Ontology-guided Storage system abilities

TL;DR

This paper explores ontology-guided data partitioning strategies for RDF triple storage, aiming to improve performance, scalability, and redundancy, and introduces semantic query rewriting rules for OWL inferences.

Contribution

It proposes a roadmap for RDF data partitioning, analyzes ontology-guided partitioning, and introduces semantic query rewriting rules for enhanced querying with OWL inferences.

Findings

Preliminary results on ontology-guided partitioning show promising performance improvements.

Semantic query rewriting rules facilitate efficient querying with OWL inferences.

The proposed roadmap guides future research in scalable RDF storage solutions.

Abstract

The vision of the Semantic Web is becoming a reality with billions of RDF triples being distributed over multiple queryable end-points (e.g. Linked Data). Although there has been a body of work on RDF triples persistent storage, it seems that, considering reasoning dependent queries, the problem of providing an efficient, in terms of performance, scalability and data redundancy, partitioning of the data is still open. In regards to recent data partitioning studies, it seems reasonable to think that data partitioning should be guided considering several directions (e.g. ontology, data, application queries). This paper proposes several contributions: describe an overview of what a road map for data partitioning for RDF data efficient and persistent storage should contain, present some preliminary results and analysis on the particular case of ontology-guided (property hierarchy) partitioning and finally introduce a set of semantic query rewriting rules to support querying RDF data needing OWL inferences