By Topic

Research on Scalable Case Representation and Its Retrieval Based on Description Logic

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Fuhua Shang ; Daqing Pet. Inst., Daqing, China ; Gang Chen ; Jing Wang ; Xingming Wang

In recent years, the combination of case-based reasoning (CBR) and domain knowledge has become a hotspot in CBR field. Different knowledge representation ways bring about different influences of CBR system performance. This paper makes a summarization and an analysis of deficiencies in integration between traditional knowledge representation and CBR, based on which it proposes a scalable case representation model using description logic (DL). Moreover, the corresponding case retrieval algorithm is also presented. The domain knowledge in CBR system mainly serves for the case retrieval and revision. Furthermore, the integration usage of domain-knowledge should be determined according to the actual demand and application background .These are the focus of emphasis of the presented model .Keeping the CBR system's merits, the model provides a approach to flexible use of domain knowledge.

Published in:

Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on  (Volume:1 )

Date of Conference:

Nov. 30 2009-Dec. 1 2009