Building concept maps by adapting semantic distance metrics to Wikipedia

Abstract

Building and checking concept maps is an active research topic in visual learning. Concept maps are intended to show visual representations of interrelated concepts in educational and professional settings. For the last decades, numerous formulas have been proposed to compute the semantic proximity between any pair of concepts in the map. A review of the employment of semantic distances in concept map construction shows the lack of a clear criterion to select a suitable formula. Traditional metrics can be basically grouped depending on the representation of their knowledge source: statistic approaches based on co-occurrence of words in big corpora; path-based methods using lexical structures, like taxonomies; and multi-source methods which combine statistic approaches and path-based methods. On the one hand, path-based measures give better results than corpora-based metrics, but they cannot be used to process specific concepts or proper nouns due to the limited vocabulary of the taxonomies used. On the other side, information obtained from big corpora-including the World Wide Web-is not organized in a specific way and natural language processing techniques are usually needed in order to obtain acceptable results. In this research Wikipedia is proposed since it does not have such limitations. This article defines an approach to adapt path-based semantic similarity measures to Wikipedia for building concept maps. Experimental evaluation with a well-known set of human similarity judgments shows that the Wikipedia adapted metrics obtains equal or even better results when compared with the non-adapted approaches.