| Abstract: | Knowledge acquisition is one of the most important and problematic aspects of developing knowledge-based systems. Many automated tools have been introduced in the past, however, manual techniques are still heavily used. Interviewing is one of the most commonly used manual techniques for a KA process, however, few automated support or tools exist to help knowledge engineers enhance their performance. This dissertation proposes a KA process model in which the knowledge engineer can effectively retrieve, structure, and formalize knowledge components, so that the resulting knowledge base is more accurate and complete. The approach proposed in this work is a hybrid of information retrieval and machine learning techniques. Two IR techniques employing best-match strategies are used; the vector space model and the probabilistic ranking principle model. A prototype of the KA model. Knowledge Acquisition process Model - Structuring Knowledge from Unstructured Data (KAM -SCUD), was implemented to demonstrate the concept. The results from KAM-SCUD were compared with the outputs from a manual KA process in terms of amount of information retrieved and the process time spent. An analysis of the results shows that the process time to retrieve knowledge components (e.g., facts, rules, protocols, uncertainty) of KAM-SCUD is about half that of the manual process and the number of knowledge components retrieved from KA activities is four times more than that retrieved through a manual process. KAM-SCUD demonstrates the effectiveness of the KA process model proposed in this dissertation. |
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