題名: Dynamic EMCUD for knowledge acquisition
作者: Lin, Shun-Chieh;Tseng, Shian-Shyong;Teng, Chia-Wen 貢獻者: Department of Information Science and Applications
關鍵詞: Classification (of information);Computer supported cooperative
work;Knowledge based systems;Time series analysis;Attribute Ordering Table (AOT);Dynamic knowledge;Knowledge explosion;Repertory Grid technique;Trend analysis;Worm detection
日期: 2008
上傳時間: 2010-04-07T13:34:15Z 出版者: Asia University
摘要: Due to the knowledge explosion, the new objects will be evolved in a dynamic environment. Hence, the knowledge can be classified into static knowledge and dynamic knowledge. Although many knowledge acquisition methodologies, based upon the Repertory Grid technique, have been proposed to systematically elicit useful rules from static grid from domain experts, they lack the ability of grid evolution to
incrementally acquire the dynamic knowledge of new evolved objects. In this paper, we propose dynamic EMCUD, a new Repertory Grid-based knowledge acquisition methodology to elicit the embedded meanings of knowledge (embedded rules bearing on m objects and k object
attributes), to enhance the ability of original EMCUD to iteratively
integrate new evolved objects and new added attributes into the original Acquisition Table (AT) and original Attribute Ordering Table (AOT). The AOT records the relative importance of all attribute to each object in EMCUD to capture the embedded meanings with acceptable certainty factor value by relaxing or ignoring some minor attributes. In order to discover the new evolved objects, a collaborative framework including local knowledge based systems (KBSs) and a collaborative KBS is proposed to analyze the correlations of inference behaviors of
embedded rules between multiple KBSs in a dynamic environment. Each KBS monitors the frequent inference behaviors of interesting embedded rules to construct a small AT increment to facilitate the acquisition of dynamic knowledge after experts confirming the new evolved objects.
Moreover, the significance of knowledge may change after a period of time, a trend of all attributes to each evolved object is used to construct a new AOT increment to help experts automatically adjust the relative importance of each attribute to each object using time series analysis
approach. Besides, three cases are considered to assist experts in adjusting the certainty factor values of the dynamic knowledge of the new evolved objects from the collection of inference logs in the collaborative KBS. To evaluate the performance of dynamic EMCUD in incrementally integrating new knowledge into the knowledge base, a worm detection prototype system is implemented.
