Persistent Link:
http://hdl.handle.net/10150/105657
Title:
Element Matching in Concept Maps
Author:
Marshall, Byron; Madhusudan, Therani
Citation:
Element Matching in Concept Maps 2004,
Publisher:
ACM
Issue Date:
2004
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105657
Submitted date:
2004-08-31
Abstract:
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.
Type:
Conference Paper
Language:
en
Keywords:
Knowledge Representation
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab; Concept mapping; Education

Full metadata record

DC FieldValue Language
dc.contributor.authorMarshall, Byronen_US
dc.contributor.authorMadhusudan, Theranien_US
dc.date.accessioned2004-08-31T00:00:01Z-
dc.date.available2010-06-18T23:31:24Z-
dc.date.issued2004en_US
dc.date.submitted2004-08-31en_US
dc.identifier.citationElement Matching in Concept Maps 2004,en_US
dc.identifier.urihttp://hdl.handle.net/10150/105657-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractConcept 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherACMen_US
dc.subjectKnowledge Representationen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherConcept mappingen_US
dc.subject.otherEducationen_US
dc.titleElement Matching in Concept Mapsen_US
dc.typeConference Paperen_US
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