Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Linked data on the web (LDOW2008)
Proceedings of the 17th international conference on World Wide Web
High-performance computing applied to semantic databases
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
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As semantic datasets grow to be very large and divergent, there is a need to identify and exploit their inherent semantic structure for discovery and optimization. Towards that end, we present here a novel methodology to identify the semantic structures inherent in an arbitrary semantic graph dataset. We first present the concept of an extant ontology as a statistical description of the semantic relations present amongst the typed entities modeled in the graph. This serves as a model of the underlying semantic structure to aid in discovery and visualization. We then describe a method of ontological scaling in which the ontology is employed as a hierarchical scaling filter to infer different resolution levels at which the graph structures are to be viewed or analyzed. We illustrate these methods on three large and publicly available semantic datasets containing more than one billion edges each.