Voting theory for concept detection

Amal Zouaq, Dragan Gasevic, Marek Hatala

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationThe Semantic Web
Subtitle of host publicationResearch and Applications - 9th Extended Semantic Web Conference, ESWC 2012, Proceedings
Number of pages15
Publication statusPublished - 6 Jun 2012
Externally publishedYes
Event9th Extended Semantic Web Conference, ESWC 2012 - Heraklion, Crete, Greece
Duration: 27 May 201231 May 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7295 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th Extended Semantic Web Conference, ESWC 2012
CityHeraklion, Crete


  • Concept extraction
  • graph-based metrics
  • ontology learning
  • social choice theory
  • voting theory

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