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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105657 |
| Date | January 2004 |
| Creators | Marshall, Byron, Madhusudan, Therani |
| Publisher | ACM |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Conference Paper |
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