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Element Matching in Concept Maps

Artificial Intelligence Lab, Department of MIS, University of Arizona / Concept maps (CM) are informal, semantic, node-link conceptual graphs used to represent knowledge in a variety of applications. Algorithms that compare concept maps would be useful in supporting educational processes and in leveraging indexed digital collections of concept maps. Map comparison begins with element matching and faces computational challenges arising from vocabulary overlap, informality, and organizational variation. Our implementation of an adapted similarity flooding algorithm improves matching of CM knowledge elements over a simple string matching approach.

Identiferoai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105657
Date January 2004
CreatorsMarshall, Byron, Madhusudan, Therani
PublisherACM
Source SetsUniversity of Arizona
LanguageEnglish
Detected LanguageEnglish
TypeConference Paper

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