Knowledge management, Artificial intelligence, Knowledge-based systems Government agencies carry out many events each year designed to determine future requirements and capabilities. These events indude field experiments, surveys, interviews, simulations and workshops. Similar themes are evident across many of these events. Unfortunately, mechanisms for passing information from one event to the next, or for developing bodies of knowledge in the topical areas they address, have yet to be fully developed. The task is difficult on two fronts. In response to this need a knowledge management capability was developed to help provide structure for dynamic and static data and thereby, aid in the analysis of complex experimentation. The system warehouses qualitative and quantitative data and supports mining operations through a number of traditional and artificial intelligence-based techniques. Described are the information architecture of the system, the knowledge processing methodologies, and the structure of the thematic data sets that form the knowledge ontologies.The research register for this journal is available at

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Know[edge anuiytics addresses processesfor the analysis of integrated yuarztitative datu and qualitutive informa-tion, ndrhin structured and unstructured media. A military experimentation knowledge analytics system and processing methodology is advanced as an instance of krzowledge analytics. The referenced howledge system was developed to unaiyze new information and communication teclinologies and supporting orgunizatiorial processes. In militaly experimentation the knowledge is devetoped iteratively such thatfindings of prior experimettts are integruted into future experimentation in a highly dynamic cycle. The niethodology addresses knowledge processing and the transition of datu into inforniation and information into knowledge. A feoture of the upprouch is the capacity for reach-back and thread analysis to trace knowledge ifenis back to source data, and forward to recoinniendarion, decision and action. very large [3]. Problems arise in knowledge processing, knowledge organization, and knowledge representation. Knowledge processing involves two major issues: access to expertise from expert sources, and electronic representation of accumulated knowledge [4]. Representation of knowledge involves the placement of knowledge items in a cohesive context, while personalization of knowledge involves facilitation of knowledge exchange [5], generally through email, warehouses, knowledge management systems, group support systems, etc. Tools have been developed to make knowledge processing as automatic and accurate as possible, however, representation problems still exist. These problems often arise from differences in knowledge structures, processing flows, and representation. A comprehensive know Ledge analytics methodology must support various data and information organizational structures. Information that 0-7803-881

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