Context-Aware, adaptive information retrieval for investigative tasks

We are building an intelligent information system to aid users in their investigative tasks, such as detecting fraud. In such a task, users must progressively search and analyze relevant information before drawing a conclusion. In this paper, we address how to help users find relevant informa-tion during an investigation. Specifically, we present a novel approach that can improve information retrieval by exploiting a user's investigative context. Compared to existing retrieval systems, which are either context insensitive or leverage only limited user context, our work offers two unique contributions. First, our system works with users cooperatively to build an investigative context, which is otherwise very difficult to capture by machine or human alone. Second, we develop a context-aware method that can adaptively retrieve and evaluate information relevant to an ongoing investigation. Experiments show that our approach can improve the relevance of retrieved information significantly. As a result, users can fulfill their investigative tasks more efficiently and effectively.

[1]  Steven K. Feiner,et al.  Dynamic space management for user interfaces , 2000, UIST '00.

[2]  Michelle X. Zhou,et al.  Interactive Visual Synthesis of Analytic Knowledge , 2006, 2006 IEEE Symposium On Visual Analytics Science And Technology.

[3]  Krishna Bharat SearchPad: explicit capture of search context to support Web search , 2000, Comput. Networks.

[4]  Farzin Maghoul,et al.  Y!Q: contextual search at the point of inspiration , 2005, CIKM '05.

[5]  Kristian J. Hammond,et al.  Information access in context: experiences with the watson system , 2003 .

[6]  Alexander W. Skaburskis,et al.  The Sandbox for analysis: concepts and methods , 2006, CHI.

[7]  Richards J. Heuer,et al.  Psychology of Intelligence Analysis , 1999 .

[8]  Edward A. Fox,et al.  Connecting topics in document collections with stepping stones and pathways , 2005, CIKM '05.

[9]  Michelle X. Zhou,et al.  An optimization-based approach to dynamic visual context management , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[10]  Xuehua Shen,et al.  Context-sensitive information retrieval using implicit feedback , 2005, SIGIR '05.

[11]  John D. Lowrance,et al.  Capturing analytic thought , 2001, K-CAP '01.

[12]  Ed Huai-hsin Chi,et al.  Entity Workspace: An Evidence File That Aids Memory, Inference, and Reading , 2006, ISI.

[13]  Charles L. A. Clarke,et al.  Modeling task-genre relationships for IR in the workplace , 2005, SIGIR '05.

[14]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[15]  Pawan Kumar,et al.  Notice of Violation of IEEE Publication Principles The Anatomy of a Large-Scale Hyper Textual Web Search Engine , 2009 .

[16]  Susan T. Dumais,et al.  Fast, flexible filtering with phlat , 2006, CHI.

[17]  Jaana Kekäläinen,et al.  IR evaluation methods for retrieving highly relevant documents , 2000, SIGIR '00.

[18]  Hsinchun Chen,et al.  COPLINK Center: Information and Knowledge Management for Law Enforcement , 2004, DG.O.

[19]  Susan T. Dumais,et al.  Personalizing Search via Automated Analysis of Interests and Activities , 2005, SIGIR.