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• W2011899702 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." @default.
• W2011899702 created "2016-06-24" @default.
• W2011899702 creator A5019982292 @default.
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• W2011899702 date "2009-03-01" @default.
• W2011899702 modified "2023-09-27" @default.
• W2011899702 title "VODKA: Variant objects discovering knowledge acquisition" @default.
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• W2011899702 doi "https://doi.org/10.1016/j.eswa.2007.12.055" @default.
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