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 eciently 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 rst prototype.
The encoding of ontology elements, @math , concepts and properties, used in our system is related to a third approach which consists in encoding elements in a clever way that retains the subsumption hierarchy. This is the approach presented in @cite_9 and implemented in the Quest system (a relational database management system). The work of Rodriguez-Muro @math @cite_9 relies on integer identifiers modeling the subsumption relationships which are being used to rewrite SQL queries ranging over identifiers intervals, @math , specifying boundaries over indexed fields in the WHERE clause of a SQL query. In comparison, our work tackles the encoding at the bit level and focuses on the sharing of common prefixes in the encoding of the identifiers (see Section 4.1). This approach allows us to rewrite the queries in terms of and operations, @math , searching for a pattern corresponding to some of the most significant bits of a concept or property identifier. Furthermore, it allows high rate compression and does not require extra specific indexing processes.
@cite_9
1804717298
Current techniques for query answering over DL-Lite ontologies have severe limitations in practice, since they either produce complex queries that are inefficient during execution, or require expensive data pre-processing. In light of this, we present two complementary sets of results that aim at improving the overall peformance of query answering systems. We show how to create ABox repositories that are complete w.r.t. a significant portion of DL-Lite TBoxes, including those expressed in RDFS, but where the data is not explicitly expanded. Second, we show how to characterize ABox completeness by means of dependencies, and how to use these and equivalence to optimize DL-Lite TBoxes. These results allow us to reduce the cost of query rewriting, often dramatically, and to generate highly efficient queries. We have implemented a novel system for query answering over DL-Lite ontologies that incorporates these techniques, and we present a series of data-intensive evaluations that show their effectiveness.