Information DiscovEry Assistant that Learns (IDEAL)

Traditional keyword search fails to adequately meet the needs of the modern intelligence analyst. If analysts were able to express their information needs in plain terms, understood by a search engine as guidance or examples, documents and other information artifacts might be brought to light that simply"guessing at"appropriate keywords would never elicit. Further, because analysts seldom work in isolation, a shared understanding of analysis goals and subsequent sharing of knowledge and effort can significantly improve analytic outcomes. KBSI proposes to design, configure, and demonstrate an innovative solution called the Information DiscovEry Assistant that Learns (IDEAL) that addresses the challenges described above. IDEAL will be designed to provide the following advanced capabilities (1) perform semantic search across multiple data sources to discover data that is relevant to an analyst"s goals and tasks; (2) progressively refine the quality of information produced through iterative semantic search and knowledge discovery; (3) facilitate dynamic information sharing and collaboration by a group of human agents; and (4) provide learning and adaptation by using the results and analyst feedback from the previous search/analysis activities to incrementally refine the semantic mapping and search models over extended time.