Elizabeth Whitaker, Robert Simpson
We are working on a project aimed at building next generation analyst support tools that focus analysts’ attention on the most critical and novel information found within the data,, thus helping analysts deal with the information overload problem. .This paper discusses the Case-based Reasoning for Knowledge Discovery (CBR for KD) which is designed to support the Internet-based search and information gathering activities of information analysts. An information analyst gathers, organizes and analyzes information and based on that analysis, makes predictions that can be used for decision making. Because of the huge volumes of data that information analysts must search, effective information gathering on the web is a complex activity requiring planning, text processing, and interpretation of extracted data to find information relevant to a major analysis task or subtask (Etzioni and Weld, 1994), (Knoblock, 1995), (Lesser, 1998) and (Nodine, Fowler et al., 2000). We have identified knowledge discovery plan categories that correspond to different contextual domains which provide analysts with indications of activities of potential interest, opportunity or threat. Using a case-based reasoning engine, a plan is selected from one of these categories and it is adapted to the current knowledge discovery problem. The resulting search plan is executed, relevant information is extracted from unstructured documents, and the extracted information is used to make further inferences and launch additional searches. This paper discusses the evolution and evaluation of the system presented in the FLAIRS 2004 paper, "Case-Based Reasoning in Support of Intelligence Analysis."
Subjects: 3.1 Case-Based Reasoning; 1. Applications
Submitted: Feb 11, 2007