Voting theory for concept detection

Authors:
Amal Zouaq;Dragan Gasevic;Marek Hatala
Affiliations:
Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON, Canada,School of Computing and Information System, Athabasca University, Athabasca, AB, Canada;School of Computing and Information System, Athabasca University, Athabasca, AB, Canada,School of Interactive Arts and Technology, Simon Fraser University, Surrey, BC, Canada;School of Interactive Arts and Technology, Simon Fraser University, Surrey, BC, Canada
Venue:
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Year:
2012

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Abstract

This paper explores the issue of detecting concepts for ontology learning from text. Using our tool OntoCmaps, we investigate various metrics from graph theory and propose voting schemes based on these metrics. The idea draws its root in social choice theory, and our objective is to mimic consensus in automatic learning methods and increase the confidence in concept extraction through the identification of the best performing metrics, the comparison of these metrics with standard information retrieval metrics (such as TF-IDF) and the evaluation of various voting schemes. Our results show that three graph-based metrics Degree, Reachability and HITS-hub were the most successful in identifying relevant concepts contained in two gold standard ontologies.