@inproceedings{7b809d175a484eadb586ed7c32867c14,
title = "Voting theory for concept detection",
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.",
keywords = "Concept extraction, graph-based metrics, ontology learning, social choice theory, voting theory",
author = "Amal Zouaq and Dragan Gasevic and Marek Hatala",
year = "2012",
month = jun,
day = "6",
doi = "10.1007/978-3-642-30284-8_28",
language = "English",
isbn = "9783642302831",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "315--329",
booktitle = "The Semantic Web",
note = "9th Extended Semantic Web Conference, ESWC 2012 ; Conference date: 27-05-2012 Through 31-05-2012",
}