DocumentCode :
1938732
Title :
Contrast Learning for Conceptual Proximity Matching
Author :
Massey, L.
Author_Institution :
R. Mil. Coll., Kingston
Volume :
7
fYear :
2007
fDate :
19-22 Aug. 2007
Firstpage :
4044
Lastpage :
4049
Abstract :
Availability of general knowledge is considered essential in intelligent systems design to avoid brittle behavior. However, knowledge is long and tedious to acquire. This paper proposes a knowledge acquisition method that allows for the acquisition of useful general knowledge for semantic matching. The approach is based on the idea that processing example cases that contrast with existing knowledge but are conceptually close provide a learning opportunity. There are many possible applications for the proposed knowledge acquisition approach including eliciting knowledge for the semantic web and semantic bridging of heterogeneous databases. We present experimental results with a set of real life examples and demonstrate that the newly acquired knowledge facilitates processing of novel cases.
Keywords :
knowledge acquisition; learning (artificial intelligence); semantic Web; conceptual proximity matching; contrast learning; knowledge acquisition; knowledge elicitation; semantic Web; semantic matching; Cybernetics; Databases; Humans; Knowledge acquisition; Logic; Machine learning; Mathematics; Programming profession; Semantic Web; Vocabulary; Knowledge acquisition; Semantic matching; Semantic proximity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
Type :
conf
DOI :
10.1109/ICMLC.2007.4370853
Filename :
4370853
Link To Document :
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