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.
