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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/18933


    Title: VODKA: Variant Objects Discovering Knowledge Acquisition,
    Authors: 曾憲雄;Tseng, Shian-Shyong
    Contributors: 資訊多媒體應用學系
    Keywords: Knowledge acquisition;Variant discovering;EMCUD;VODKA;Computer worm
    Date: 2009-03
    Issue Date: 2012-11-26 15:10:52 (UTC+8)
    Abstract: Many knowledge acquisition methodologies have been proposed to elicit rules systematically with embedded meaning from domain
    experts. But, none of these methods discusses the issue of discovering new modified objects in a traditional classification knowledge based
    system. For experts to sense the occurrence of new variants and revise the original rule base, to collect sufficient relevant information
    becomes increasingly important in the knowledge acquisition field. In this paper, the method variant objects discovering knowledge
    acquisition (VODKA) we proposed includes three stages (log collecting stage, knowledge learning stage, and knowledge polishing stage)
    to facilitate the acquisition of new inference rules for a classification knowledge based system. The originality of VODKA is to identify
    these new modified objects, the variants, from the way that the existing knowledge based system fails in applying some rules with low
    certainty degree. In this method, we try to classify the current new evolving object identified according to its attributes and their corresponding
    values. According to the analysis of the collected inference logs, one of the three recommendations (including adding a new
    attribute-value of an attribute, modifying the data type of an attribute, or adding a new attribute) will be suggested to help experts
    observe and characterize the new confirmed variants. VODKA requires E-EMCUD (extended embedded meaning capturing and
    uncertainty deciding). EMCUD is a knowledge acquisition system which relies on the repertory grids technique to manage object/attribute-values
    tables and to produce inferences rules from these tables. The E-EMCUD we used here is a new version of EMCUD to update
    existing tables by adding new objects or new attributes and to adapt the original embedded rules. Here, a computer worm detection
    prototype is implemented to evaluate the effectiveness of VODKA. The experimental results show that new worm variants could be
    discovered from inference logs to customize the corresponding detection rules for computer worms. Moreover, VODKA can be applied
    to the e-learning area to learn the variant learning behaviors of students and to reconstruct the teaching materials in improving the
    performance of e-learners.
    Relation: EXPERT SYSTEMS WITH APPLICATIONS
    Appears in Collections:[行動商務與多媒體應用學系] 期刊論文

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