WaterFowl, a Compact, Self-indexed RDF Store with Inference-enabled Dictionaries

TL;DR

WaterFowl is a compact, self-indexed RDF store leveraging succinct data structures, enabling efficient storage and inference support without decompression or full materialization, suitable for big data and Semantic Web applications.

Contribution

It introduces a novel RDF storage architecture using succinct data structures that supports inference without full materialization or extensive rewriting.

Findings

Supports RDF and RDFS entailment regimes efficiently

Achieves compact storage without decompression during queries

Preliminary evaluation shows promising performance

Abstract

In this paper, we present a novel approach -- called WaterFowl -- for the storage of RDF triples that addresses some key issues in the contexts of big data and the Semantic Web. 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 extensive query rewriting algorithms. 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 the complete architecture of this system and presents some preliminary results obtained from evaluations conducted on our first prototype.