Utilizing semantic Wiki technology for intelligence analysis at the tactical edge
|Utilizing semantic Wiki technology for intelligence analysis at the tactical edge|
|Published in||Proceedings of SPIE - The International Society for Optical Engineering|
|Keyword(s)||Big Data, Ontologies, Semantic Wiki, Soft Fusion, Tactical Edge, Triple Store (Extra: Data fusion, Natural language processing systems, Ontology, Semantics, Social sciences computing, Hard and soft data fusions, Intelligence agencies, Large amounts of data, NAtural language processing, Semantic wiki, Soft fusions, Tactical Edge, Triple store, Big data)|
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Utilizing semantic Wiki technology for intelligence analysis at the tactical edge is a 2014 conference paper written in English by Little E. and published in Proceedings of SPIE - The International Society for Optical Engineering.
Challenges exist for intelligence analysts to efficiently and accurately process large amounts of data collected from a myriad of available data sources. These challenges are even more evident for analysts who must operate within small military units at the tactical edge. In such environments, decisions must be made quickly without guaranteed access to the kinds of large-scale data sources available to analysts working at intelligence agencies. Improved technologies must be provided to analysts at the tactical edge to make informed, reliable decisions, since this is often a critical collection point for important intelligence data. To aid tactical edge users, new types of intelligent, automated technology interfaces are required to allow them to rapidly explore information associated with the intersection of hard and soft data fusion, such as multi-INT signals, semantic models, social network data, and natural language processing of text. Abilities to fuse these types of data is paramount to providing decision superiority. For these types of applications, we have developed BLADE. BLADE allows users to dynamically add, delete and link data via a semantic wiki, allowing for improved interaction between different users. Analysts can see information updates in near-real-time due to a common underlying set of semantic models operating within a triple store that allows for updates on related data points from independent users tracking different items (persons, events, locations, organizations, etc.). The wiki can capture pictures, videos and related information. New information added directly to pages is automatically updated in the triple store and its provenance and pedigree is tracked over time, making that data more trustworthy and easily integrated with other users' pages.
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