Ontologizing concept maps using graph theory

Amal Zouaq; Dragan Gasevic; Marek Hatala

doi:10.1145/1982185.1982537

Ontologizing concept maps using graph theory

Amal Zouaq, Dragan Gasevic, Marek Hatala

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

14 Citations (Scopus)

Abstract

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 language	English
Title of host publication	26th Annual ACM Symposium on Applied Computing, SAC 2011
Pages	1687-1692
Number of pages	6
DOIs	https://doi.org/10.1145/1982185.1982537
Publication status	Published - 23 Jun 2011
Externally published	Yes
Event	ACM Symposium on Applied Computing 2011 - Tunghai University, Taichung, Taiwan Duration: 21 Mar 2011 → 24 Mar 2011 Conference number: 26th http://www.sigapp.org/sac/sac2011/ https://dl.acm.org/doi/proceedings/10.1145/1982185 (Proceedings)

Conference

Conference	ACM Symposium on Applied Computing 2011
Abbreviated title	SAC 2011
Country/Territory	Taiwan
City	Taichung
Period	21/03/11 → 24/03/11
Internet address	http://www.sigapp.org/sac/sac2011/ https://dl.acm.org/doi/proceedings/10.1145/1982185 (Proceedings)

Keywords

concept and relation importance
graph theory
metrics
ontologization
ontology
precision

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10.1145/1982185.1982537

Cite this

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title = "Ontologizing concept maps using graph theory",

abstract = "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.",

keywords = "concept and relation importance, graph theory, metrics, ontologization, ontology, precision",

author = "Amal Zouaq and Dragan Gasevic and Marek Hatala",

year = "2011",

month = jun,

day = "23",

doi = "10.1145/1982185.1982537",

language = "English",

isbn = "9781450301138",

pages = "1687--1692",

booktitle = "26th Annual ACM Symposium on Applied Computing, SAC 2011",

note = "ACM Symposium on Applied Computing 2011, SAC 2011 ; Conference date: 21-03-2011 Through 24-03-2011",

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AU - Gasevic, Dragan

AU - Hatala, Marek

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Y1 - 2011/6/23

N2 - 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.

AB - 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.

KW - concept and relation importance

KW - graph theory

KW - metrics

KW - ontologization

KW - ontology

KW - precision

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M3 - Conference Paper

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BT - 26th Annual ACM Symposium on Applied Computing, SAC 2011

T2 - ACM Symposium on Applied Computing 2011

Y2 - 21 March 2011 through 24 March 2011

ER -

Ontologizing concept maps using graph theory

Abstract

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Keywords

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