Ontology-Based Knowledge Management for Artificial Intelligent Systems

Abstract

One area of artificial intelligence system design, development, and especially testing is the notion of knowledge management. Long-term memory within an Artificial Intelligent System (AIS) can be thought of as the system's Knowledge Base (KB) or knowledge ontology (Newell et al., Preliminary description of general problem-solving program-i (gps-i). Carnegie Institute of Technology, Pittsburgh, PA, 1957). What does the AIS know, what has it learned, and how does that knowledge change over time as the system continues to learn, reason, and adapt. How are memories modified or reinterpreted as new information is gained through experience as the system interacts with its environment? How knowledge is managed throughout the AIS radically changes how it learns, what it learns, and how it assimilates knowledge. This must be considered early on, even during the overall conceptual modeling of the AIS. Ontologies are an important part of conceptual modeling. They provide substantial structural information and are typically the key elements in any large-scale integration effort (Davis et al., Molecular Psychiatry 6:13--34, 2001). We investigate and present several notions from the formal practice of ontology and adapt them for use in intelligence processing and fusion systems. The aim is to provide a solid logical framework that will allow a formal taxonomy to be analyzed. We will provide examples of formal ontological analysis applied to system of systems fault ontologies and an example of technical publications taxonomy.