Automated Concept Map Elicitation (ACME) Navy SBIR FY2013.2 Sol No.: Navy SBIR FY2013.2 Topic No.: N132-128 Topic Title: Automated Concept Map Elicitation (ACME) Proposal No.: N132-128-0654 Firm: DECISIVE ANALYTICS Corporation 1235 South Clark Street Suite 400 Arlington, Virginia 22202 Contact: Jonathan Day Phone: (703) 414-5015 Web Site: http://www.dac.us Abstract: Rapid response missions to remote, unknown areas are becoming a primary focus for U.S. military forces. These missions require time-sensitive development of intelligence from all available sources including open source data, historic imagery, and live collections. Capabilities currently exist to extract low-level information (i.e. entities, relationships, and actions) from these large scale data sources. However, most of the intelligence requirements that need to be fulfilled are of a high-level conceptual nature. The fulfillment of these intelligence requirements needs a system that can utilize the extracted low-level information and the context surrounding this information to provide concept-level knowledge generation. Therefore, DAC proposes to develop a system called Automated Concept Map Elicitation (ACME). The ACME system will be focused on providing users with a rapid, visual mechanism for developing situational awareness around a specific intelligence requirement. To provide this capability, the ACME system will be built to support the extraction of information stored across numerous ontologies, utilize automated clustering of entity nodes and relationships to simplify the developed concept maps, and include an intuitive visualization of concept maps based on knowledge pertinent to the specific intelligence requirement. Benefits: The key benefits of the proposed solution are the ability to utilize information stored across numerous ontologies, the ability for the user to request the development of a concept map via a plain English question, a simplified display of information supported by automated clustering of entities, relations, and actions, and an intuitive visualization focused on supporting user interaction including the ability to modify the underlying data based on the user's expert knowledge. Return