KAMET: A comprehensive methodology for knowledge acquisition from multiple knowledge sources

At the beginning of the 1980s, the Artificial Intelligence (AI) community showed little interest in research on methodologies for the construction of knowledge-based systems (KBS) and for knowledge acquisition (KA). The main idea was the rapid construction of prototypes with LISP machines, expert system shells, and so on. Over time, the community saw the need for a structured development of KBS projects, and KA was recognized as the critical stage and the bottleneck for the construction of KBS. Concerning KA, many publications have appeared since then. However, very few have focused on formal plans to manage knowledge acquisition and from multiple knowledge sources. This paper addresses this important problem. KAMET is a formal plan based on models designed to manage knowledge acquisition from multiple knowledge sources. The objective of KAMET is to improve, in some sense, the phase of knowledge acquisition and knowledge modeling process, making them more efficient.

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