Ontologizing concept maps using graph theory

Amal Zouaq, Dragan Gasevic, Marek Hatala

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

13 Citations (Scopus)


Given the new challenges of open and unsupervised information extraction, there is a need to identify important and relevant knowledge structures (concepts and relationships) in the vast amount of extracted data and to filter the noise that results from unsupervised information extraction. This is generally referred to as the ontologization task. This paper uses measures from graph theory to identify these key elements such as Page Rank, Betweenness, and Degree. We also propose a combination of metrics for ranking concepts and relationships. Our approach shows effective results in terms of precision compared to other standard measures for weighting concepts and relationships such as TF*IDF or frequency of co-occurrences.

Original languageEnglish
Title of host publication26th Annual ACM Symposium on Applied Computing, SAC 2011
Number of pages6
Publication statusPublished - 23 Jun 2011
Externally publishedYes
EventACM Symposium on Applied Computing 2011 - Tunghai University, Taichung, Taiwan
Duration: 21 Mar 201124 Mar 2011
Conference number: 26th
https://dl.acm.org/doi/proceedings/10.1145/1982185 (Proceedings)


ConferenceACM Symposium on Applied Computing 2011
Abbreviated titleSAC 2011
Internet address


  • concept and relation importance
  • graph theory
  • metrics
  • ontologization
  • ontology
  • precision

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