Collaborative knowledge discovery &amp;#x00026; marshalling for intelligence &amp;#x00026; security applications

Cowell, Andrew; Jensen, Russell S.; Gregory, Michelle; Ellis, Peter; Fligg, Keith; McGrath, Liam R.; O'Hara, Kelly; Bell, Eric B.

doi:10.1109/isi.2010.5484731

Cited by 1 publication

(1 citation statement)

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“…The first sentence talks about a real and urgent threat of nuclear terrorism while the second suggests that the international community's pace to respond to that threat is insufficient compared to the urgency of the threat. The next step is to expand from sentences to paragraphs while maintaining the level of accuracy experienced at the sentence level [45].…”

Section: Stage 4: Evidence-model Alignmentmentioning

confidence: 99%

Knowledge encapsulation framework for technosocial predictive modeling

et al. 2012

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Analysts who use predictive analytics methods need actionable evidence to support their models and simulations. Commonly, this evidence is distilled from large data sets with significant amount of culling and searching through a variety of sources including traditional and social media. The time/cost effectiveness and quality of the evidence marshaling process can be greatly enhanced by combining component technologies that support directed content harvesting, automated semantic annotation, and content analysis within a collaborative environment, with a functional interface to models and simulations. Existing evidence extraction tools provide some, but not all, the critical components that would empower such an integrated knowledge management environment. This paper describes a novel evidence marshaling solution that significantly advances the state of the art. Its embodiment, the Knowledge Encapsulation Framework (KEF), offers a suite of semi-automated and configurable content harvesting, vetting, annotation and analysis capabilities within a wiki-enabled and user-friendly visual interface that supports collaborative work across distributed teams of analysts. After a summarization of related work, our motivation, and the technical implementation of KEF, we will explore the model for using KEF and results of our research.

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Section: Stage 4: Evidence-model Alignmentmentioning

confidence: 99%