WaterFowl: A compact, self-indexed and inference-enabled immutable RDF store

Abstract

In this paper we present WaterFowl, a novel approach for the storage of RDF triples that addresses scalability issues through compression. The architecture of our prototype, largely based on the use of succinct data structures, enables the representation of triples in a self-indexed, compact manner without requiring decompression at query answering time. Moreover, it is adapted to efficiently support RDF and RDFS entailment regimes thanks to an optimized encoding of ontology concepts and properties that does not require a complete inference materialization or query reformulation. This approach implies to make a distinction between the terminological and the assertional components of the knowledge base early in the process of data preparation, i.e., preprocessing the data before storing it in our structures. The paper describes our system's architecture and presents some preliminary results obtained from evaluations on different datasets. © 2014 Springer International Publishing.