Matching knowledge elements in concept maps using a similarity flooding algorithm

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. Key words: Semantic Matching; Concept Mapping; Semantic Networks; Conceptual Graphs; Computer Assisted Instruction

• Type: Journal
• Year: 2006
• Where: DSS
• Authors: Byron Marshall, Hsinchun Chen, Therani Madhusudan
• Keywords: Concept Mapping, Concept Mapping Systems, Concept Maps, DSS 2006