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10.1109/ICEBE.2006.48guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
Article

Evaluation Metrics for Ontology Complexity and Evolution Analysis

Published: 24 October 2006 Publication History

Abstract

With the tremendous development in size, the complexity of ontology increases. Thus ontology evaluation becomes extremely important for developers to determine the fundamental characteristics of ontologies in order to improve the quality, estimate cost and reduce future maintenance. Our research examines the concepts and their hierarchy in conceptual model, the common feature of the most ontologies, which reflects the fundamental complexity. We suggest a well-defined metrics suite of complexity, which mainly examine the quantity, ratio and correlativity of concepts and relationships, to evaluate ontologies from the viewpoint of complexity and its evolution. In the study, we measure three ontologies in GO to verify our metrics. The results indicate that this metrics suite works well, and the biological process ontology is the most complex one from the view of complexity, and the molecular function ontology is the unsteadiest one from the view of evolution.

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cover image Guide Proceedings
ICEBE '06: Proceedings of the IEEE International Conference on e-Business Engineering
October 2006
674 pages
ISBN:0769526454

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IEEE Computer Society

United States

Publication History

Published: 24 October 2006

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  • (2019)Evaluating Domain OntologiesACM Computing Surveys10.1145/332912452:4(1-44)Online publication date: 11-Sep-2019
  • (2018)Who Models the World?Proceedings of the ACM on Human-Computer Interaction10.1145/32744102:CSCW(1-18)Online publication date: 1-Nov-2018
  • (2016)Towards measurement of structural complexity for ontologiesInternational Journal of Web Engineering and Technology10.1504/IJWET.2016.07734311:2(153-173)Online publication date: 1-Jan-2016
  • (2015)An Analysis of Applicability using Quality Metrics for Ontologies on Ontology Design PatternsInternational Journal of Intelligent Systems in Accounting and Finance Management10.1002/isaf.136022:1(81-99)Online publication date: 1-Jan-2015
  • (2012)Predicting reasoning performance using ontology metricsProceedings of the 11th international conference on The Semantic Web - Volume Part I10.1007/978-3-642-35176-1_13(198-214)Online publication date: 11-Nov-2012
  • (2011)Context-based citation retrievalInternational Journal of Networking and Virtual Organisations10.1504/IJNVO.2011.0371638:1/2(98-122)Online publication date: 1-Nov-2011

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