Extension CBR Retrieval

Author

Ni, Zhiwei ; Han, Dan ; Zhang, Gongrang ; Gao, Yazhuo

Author_Institution

Key Lab. of Process Optimization & Intell. Decision-making, Minist. of Educ., Hefei Univ. of Technol., Hefei, China

Volume

3

fYear

2009

fDate

7-8 Nov. 2009

Firstpage

224

Lastpage

227

Abstract

When the case information is described in natural language and not complete, to deal with problems of case retrieval in Case-Based Reasoning (CBR), Extension theory is introduced to aid it. Firstly, the case index is created based on the Extension model. Secondly, the main characteristics of the case are formalized to store in the case base to realize the data compression. Finally, case retrieval algorithm is designed. The basic and the advanced retrieve strategy supplement each other. This algorithm lowers the request of the professional level and improves the efficiency of retrieval. The approach is of practical significance in problem solving, such as auto fault diagnosis.

Keywords

case-based reasoning; content-based retrieval; data compression; natural language processing; problem solving; CBR retrieval; advanced retrieve strategy; case retrieval algorithm; case-based reasoning; data compression; extension theory; natural language; problem solving; Algorithm design and analysis; Artificial intelligence; Competitive intelligence; Computational intelligence; Data compression; Fault diagnosis; Information retrieval; Intelligent systems; Mathematics; Problem-solving; CBR; case retrieval; data compression; extension model;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-3835-8

Electronic_ISBN

978-0-7695-3816-7

Type

conf

DOI

10.1109/AICI.2009.278

Filename

5376620