Structure Discovery in Large Semantic Graphs Using Extant Ontological Scaling and Descriptive Semantics
Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on (2011)
Aug. 22, 2011 to Aug. 27, 2011
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.
Semantic Web, Visualization, Ontology, Multiresolution Data Mining
Cliff Joslyn, Alan Chappell, Sinan al-Saffar, "Structure Discovery in Large Semantic Graphs Using Extant Ontological Scaling and Descriptive Semantics", Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on, vol. 01, no. , pp. 211-218, 2011, doi:10.1109/WI-IAT.2011.241