This paper describes extending the automated discovery mechanism of the Knowledge Encapsulation Framework (KEF) through the use of agent technology. KEF is a suite of tools to enable the linking of knowledge inputs (relevant, domain-specific evidence) to modeling and simulation projects, as well as other domains that require an effective collaborative workspace for knowledge-based tasks. This framework can be used to capture evidence (e.g., trusted material such as journal articles and government reports), discover new evidence (covering both trusted and social media), enable discussions surrounding domain-specific topics and provide automatically generated semantic annotations for improved corpus investigation. The current KEF design is described along with the new agent based knowledge management system, which addresses the weaknesses of the current knowledge acquisition approach.

Intelligence analysts are bombarded with enormous volumes of imagery that they must visually filter to identify relevant areas of interest. Interpretation of such data is subject to error due to (1) large data volumes, implying the need for faster and more effective processing, and (2) misinterpretation, implying the need for enhanced analyst/system effectiveness. This paper outlines the Revolutionary Accelerated Processing Image Detection (RAPID) System, designed to significantly improve data throughput and interpretation by incorporating advancing neurophysiological technology to monitor processes associated with detection and identification of relevant target stimuli in a non-invasive and temporally precise manner. Specifically, this work includes the development of innovative electroencephalographic (EEG) and eye tracking technologies to detect and flag areas of interest, potentially without an analyst's conscious intervention or motor responses, while detecting and mitigating problems with tacit knowledge, such as anchoring bias in real-time to reduce the possibility of human error.

— The Intelligence Community and other analytic-focused communities are developing and implementing large knowledge bases and semantic-based systems. These systems require new activities for managing their ontological underpinning, including a range of tasks from supporting domain description and evolution to integrating multiple source of semantic information. Beyond the role of the analyst or the traditional data base administrator, the role of the knowledge manager as the point of focus for such activities is growing in prominence. We are developing methods and tools to provide an analytical ability for the display and management of ontological systems, rooted in the formal properties of semantic relations in semantic graphs, and the semantic hierarchies in which they are valued. We describe methods for display, integration, and management of ontological resources to support the emerging Analytical Knowledge Manager with the AKEA tool.