Supporting insight-based information exploration in intelligence analysis

Gersh, John; Lewis, Bessie; Montemayor, Jaime; Piatko, Christine D.; Turner, Russell James

doi:10.1145/1121949.1121984

Cited by 24 publications

(17 citation statements)

References 7 publications

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“…Intelligence analysis: Research work into software tools for intelligence analysis can be grouped into model-guided systems [12][13][14], multi-agent systems [15], graph-based analytic tools [16][17][18][19], and collaborative systems [5,20,21]. Our work falls in the category of graph-based analytic tools.…”

Section: Related Projectsmentioning

confidence: 99%

Storytelling in entity networks to support intelligence analysts

Hossain

Butler

Boedihardjo

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Intelligence analysts grapple with many challenges, chief among them is the need for software support in storytelling, i.e., automatically 'connecting the dots' between disparate entities (e.g., people, organizations) in an effort to form hypotheses and suggest nonobvious relationships. We present a system to automatically construct stories in entity networks that can help form directed chains of relationships, with support for co-referencing, evidence marshaling, and imposing syntactic constraints on the story generation process. A novel optimization technique based on concept lattice mining enables us to rapidly construct stories on massive datasets. Using several public domain datasets, we illustrate how our approach overcomes many limitations of current systems and enables the analyst to efficiently narrow down to hypotheses of interest and reason about alternative explanations.

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Section: Related Projectsmentioning

confidence: 99%

Storytelling in entity networks to support intelligence analysts

Hossain

Butler

Boedihardjo

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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“…NameSieve is different from all these tools in its NE-based approach to visualize and explore a set of documents. While the use of NEs in intelligence analysis has been explored by a few other projects (Bier, Card, & Bodnar, 2008;Gersh, Lewis, Montemayor, Piatko, & Turner, 2006), these projects use NEs for sense-making, while NameSieve uses it for information foraging.…”

Section: Related Studiesmentioning

confidence: 99%

Semantic annotation based exploratory search for information analysts

Ahn¹,

Brusilovsky²,

Grady³

et al. 2010

Information Processing & Management

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Abstract:The system presented in this article aims to improve information access through the use of semantic annotation utilizing a non-traditional approach. Instead of applying semantic annotations to enhance the internal information access mechanisms, we use them to empower the user of an information access system through an innovative named entity-based user interface -NameSieve. NameSieve was built to support an intelligence analyst during the process of exploratory search, an advanced type of search requiring multiple iterations of retrieval interleaved with browsing and analyzing the retrieved information. The proposed approach was implemented in the NameSieve system so that the system can transparently present a summary of search results in the form of entity "clouds." Therefore, these clouds allow the analyst to further explore the results in a novel manner, acting together as a faceted browsing interface. We ran a user study (with ten subjects) to examine the effect of NameSieve, and the study results reported in the paper demonstrate that this new way of applying semantic annotation information was actively used and was evaluated positively by the subjects. It enabled the subjects to work more productively and bring back most relevant documents.

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“…The search process itself not just the search results give the necessary insight. Thus, intelligence analysis systems should support capturing and visually representing analysts' iterative query processes and insights help them collect and compare information more effectively, as well as record and share the products of their analytic insights [5].…”

Section: Intelligence Analysis and Information Retrievalmentioning

confidence: 99%