Semantic Analytics in Intelligence: Applying Semantic Association Discovery to Determine Relevance of Heterogeneous Documents
Document Type
Book Chapter
Publication Date
2005
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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 and relationships 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.
Repository Citation
Aleman-Meza, B.,
Sheth, A. P.,
Paliniswami, D.,
Eavenson, M.,
& Arpinar, I. B.
(2005). Semantic Analytics in Intelligence: Applying Semantic Association Discovery to Determine Relevance of Heterogeneous Documents. Advanced Topics in Database Research, 5, 401-419.
https://corescholar.libraries.wright.edu/knoesis/658
DOI
10.4018/978-1-59140-935-9.ch020
