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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8727

    Title: Dynamic EMCUD for knowledge acquisition
    Authors: Lin, Shun-Chieh;Tseng, Shian-Shyong;Teng, Chia-Wen
    Contributors: Department of Information Science and Applications
    Keywords: 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
    Date: 2008
    Issue Date: 2010-04-07 21:34:15 (UTC+8)
    Publisher: Asia University
    Abstract: 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.
    Relation: Expert Systems with Applications 34:833-844
    Appears in Collections:[行動商務與多媒體應用學系] 期刊論文

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