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doi:10.1016/j.dss.2005.10.009 
Copyright © 2005 Elsevier B.V. All rights reserved.
Matching knowledge elements in concept maps using a similarity flooding algorithm
Received 4 May 2005;
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Abstract
Concept mapping systems used in education and knowledge management emphasize flexibility of representation to enhance learning and facilitate knowledge capture. Collections of concept maps exhibit terminology variance, informality, and organizational variation. These factors make it difficult to match elements between maps in comparison, retrieval, and merging processes. In this work, we add an element anchoring mechanism to a similarity flooding (SF) algorithm to match nodes and substructures between pairs of simulated maps and student-drawn concept maps. Experimental results show significant improvement over simple string matching with combined recall accuracy of 91% for conceptual nodes and concept → link → concept propositions in student-drawn maps.
Keywords: Semantic matching; Concept mapping; Semantic networks; Conceptual graphs; Computer assisted instruction
Article Outline
- 1. Introduction
- 2. Literature review and background
- 2.1. Concept map (CM) applications
- 2.2. Computational challenges
- 2.2.1. Terminology variation
- 2.2.2. Informality
- 2.2.3. Organizational variation
- 2.3. Matching techniques
- 2.4. Similarity flooding
- 3. Research questions
- 4. Implementation
- 5. Simulation experiment
- 6. Student-drawn map experiment
- 7. Discussion
- 8. Conclusions and future directions
- Acknowledgements
- References
- Vitae
