WaterFowl, a Compact, Self-indexed RDF Store with Inference-enabled Dictionaries
Olivier Cur\'e, Guillaume Blin, Dominique Revuz, David Faye

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…
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Taxonomy
TopicsSemantic Web and Ontologies · Advanced Database Systems and Queries · Web Data Mining and Analysis
