Mining Large-Scale Knowledge Sources for Case Adaptation Knowledge
Identifieur interne : 000375 ( Main/Exploration ); précédent : 000374; suivant : 000376Mining Large-Scale Knowledge Sources for Case Adaptation Knowledge
Auteurs : David Leake [États-Unis] ; Jay Powell [États-Unis]Source :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: Making case adaptation practical is a longstanding challenge for case-based reasoning. One of the impediments to widespread use of automated case adaptation is the adaptation knowledge bottleneck: the adaptation process may require extensive domain knowledge, which may be difficult or expensive for system developers to provide. This paper advances a new approach to addressing this problem, proposing that systems mine their adaptation knowledge as needed from pre-existing large-scale knowledge sources available on the World Wide Web. The paper begins by discussing the case adaptation problem, opportunities for adaptation knowledge mining, and issues for applying the approach. It then presents an initial illustration of the method in a case study of the testbed system WebAdapt. WebAdapt applies the approach in the travel planning domain, using OpenCyc, Wikipedia, and the Geonames GIS database as knowledge sources for generating substitutions. Experimental results suggest the promise of the approach, especially when information from multiple sources is combined.
Url:
DOI: 10.1007/978-3-540-74141-1_15
Affiliations:
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