Semantic Analytics in Intelligence: Applying Semantic Association Discovery to Determine Relevance of Heterogeneous Documents

Semantic Analytics in Intelligence: Applying Semantic Association Discovery to Determine Relevance of Heterogeneous Documents

Boanerges Aleman-Meza (University of Georgia, USA), Amit P. Sheth (University of Georgia, USA), Devanand Palaniswami (University of Georgia, USA), Matthew Eavenson (University of Georgia, USA) and I. Budak Arpinar (University of Georgia, USA)
Copyright: © 2006 |Pages: 19
DOI: 10.4018/978-1-59140-935-9.ch020
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Abstract

We describe an ontological approach for determining the relevance of documents based on the underlying concept of exploiting complex semantic relationships among real-world entities. This research builds upon semantic metadata extraction and annotation, practical domain-specific ontology creation, main-memory query processing, and the notion of semantic association. A prototype application illustrates the approach by supporting the identification of insider threats for document access. In this scenario, we describe how investigative assignments performed by intelligence analysts are captured into a context of investigation by including concepts andrelationships from the ontology. A relevance measure for documents is computed using semantic analytics techniques. Additionally, a graph-based visualization component allows exploration of potential document access beyond the ‘need to know’. We also discuss how a commercial product using Semantic Web technology, Semagix Freedom, is used for metadata extraction when designing and populating an ontology from heterogeneous sources.

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