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

TitleSemantic Analytics in Intelligence: Applying Semantic Association Discovery to Determine Relevance of Heterogeneous Documents
Publication TypeBook Chapter
Year of Publication2006
AuthorsDevan Palaniswami, Matthew Eavenson, Ismailcem Budak Arpinar, Boanerges Aleman-Meza, Amit Sheth
Keywordscomplex relationshps, context, data security, graph traversal, insider threat, Knowledge Base, Knowledge Discovery, metadata, Ontology, ranking, rdf, relevance of information, Semantic Analytics, semantic applications for homeland security, semantic associations, semantic discovery, semantic matching, Semantic Metadata, semantic ranking, semantic relationships, Semantic Web, semantic Web technology
Abstract

Creating applications that allow users to gain insightful and actionable information or mine for interesting patterns from vast amounts of heterogeneous information is one of the most exciting new areas of information systems research. This information to be analyzed may come from numerous sources spanning proprietary, trusted, and open-source information, including intranets, the deep Web and the open Web. The fast emerging markets of business intelligence as well as national and homeland security are finding themselves in increasing need of a class of applications called risk and compliance (Sheth 2005). One representative example of this class of applications is the Insider Threat application, which involves validation of legitimate access of documents. While physical security measures may help reduce malevolent access to documents by employees within an organization, the development of new information-based security systems provides additional capabilities for defense against insider threat attacks. The intent of this application is to monitor that analysts who are assigned various investigation tasks access the information on a 'need to know basis' and that the system should identified access to irrelevant information in an attempt to reduce the chances that confidential information is leaked or released inappropriately.

Full Text

Boanerges Aleman-Meza,Amit Sheth, Devanand Palaniswami, Matthew Eavenson, and I. Budak Arpinar, 'Semantic Analytics in Intelligence: Applying Semantic Association Discovery to Determine Relevance of Heterogeneous Documents,'in Advanced Topics in Database Research, Vol. 5, Keng L. Siau (Ed.), Idea Group Publishing, 2006, pp. 401-419.
pages: pp. 401-419
publisher: Idea Group Publishing
year: 2006
hasEditor: Keng L. Siau (Ed.)
hasURL: http://knoesis.wright.edu/library/download/ASPEA05-ATDR-chapter.pdf
hasBookTitle: Advanced Topics in Database Research

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