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Comparing Similarity Calculation Methods in Conversational CBR
 

Summary: Comparing Similarity Calculation Methods in
Conversational CBR
Mingyang Gu, Xin Tong, and Agnar Aamodt
Department of Computer and Information Science, Norwegian University of Science
and Technology, Sem Saelands vei 7-9, N-7491, Trondheim, Norway
Email: mingyang,tongxin,agnar@idi.ntnu.no
Abstract-- Conversational Case-Based-Reasoning (CCBR) pro-
vides a mixed-initiative dialog for guiding users to construct their
problem description incrementally through a question-answering
sequence. Similarity calculation in CCBR, as in traditional CBR,
plays an important role in the retrieval process since it decides
the quality of the retrieved case. In this paper, we analyze the
different characteristics of the query (new case) between CCBR
and traditional CBR, and argue that the similarity calculation
method that only takes the features appearing in the query into
account, so called query-biased, is more suitable for CCBR. An
experiment is designed and executed on 36 datasets. The results
show us that on 31 datasets out of the total 36, the CCBR system
using the query-biased similarity calculation method achieves
more effective performance than those using case-biased and

  

Source: Aamodt, Agnar - Department of Computer and Information Science, Norwegian University of Science and Technology

 

Collections: Computer Technologies and Information Sciences