Supporting Scalable, Persistent Semantic Web Applications.

Abstract

To realize the vision of the Semantic Web, efficient storage and retrieval of large RDF data sets is required. A common technique for persisting RDF data (graphs) is to use a single relational database table, a triple store. But, we believe a single triple store cannot scale for large-scale applications. This paper describes storing and querying persistent RDF graphs in Jena, a Semantic Web programmers' toolkit. Jena augments the triple store with property tables that cluster multiple property values in a single table row. We also describe two tools to assist in designing application-specific RDF storage schema. The first is a synthetic data generator that generates RDF graphs consistent with an underlying ontology. The second mines an RDF graph or an RDF query log for frequently occurring patterns. These patterns can be applied to schema design or caching strategies to improve performance. We also briefly describe Jena inferencing and a new approach to context in RDF which we call snippets.