A two-phased ontology selection approach for semantic Web

Tzung Pei Hong, Wen Chang Chang, Jiann Horng Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we attempt to propose a two-phased ontology selection approach. Users can describe their requirements through a two-level requirement analysis model. The coarse-level analysis model is quite simple, only used for describing the main domains covered by the desired ontology and for selecting a fixed number of promising candidate source ontologies. The fine-level analysis model then refines the coarse-level model by defining the details in each domain. It is used to find the best matched ontology from the candidates from phase 1. The token extraction module and the WordNet system are also used to help for flexible match. The proposed selection approach can help easily find an appropriate ontology for a new application.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
Pages403-409
Number of pages7
Publication statusPublished - Dec 1 2005
Externally publishedYes
Event9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia
Duration: Sep 14 2005Sep 16 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3684 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
CountryAustralia
CityMelbourne
Period9/14/059/16/05

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hong, T. P., Chang, W. C., & Lin, J. H. (2005). A two-phased ontology selection approach for semantic Web. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings (pp. 403-409). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3684 LNAI).