Matching knowledge elements in concept maps using a similarity flooding algorithm

Matching knowledge elements in concept maps using a similarity flooding algorithm

0.00 Avg rating0 Votes
Article ID: iaor2008642
Country: Netherlands
Volume: 42
Issue: 3
Start Page Number: 1290
End Page Number: 1306
Publication Date: Dec 2006
Journal: Decision Support Systems
Authors: , ,
Keywords: artificial intelligence: decision support
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 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.

Reviews

Required fields are marked *. Your email address will not be published.