Mediating between heterogeneous ontologies using schema matching techniques

Oliver Lyttleton, David Sinclair, David Tracey

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

1 Citation (Scopus)

Abstract

The semantic web envisions an Internet where data can be used by applications just as easily as it can be by humans. Ontologies are a key building block of the semantic web, and can enable applications to have a shared understanding of data, and to be semantically interoperable. However, ontologies from heterogeneous sources may use different terms to represent the same concept. This can present problems for applications which are required to work interoperably with independently produced ontologies. Manually determining semantically equivalent terms between ontologies is a laborious and error-prone process. In this paper, we present an architecture which uses machine-learning techniques to produce mappings between semantically equivalent terms in heterogeneous ontologies. The architecture combines the results of several machine-learning algorithms, in order to be effective with a wider range of data than if a single algorithm were used.

Original languageEnglish
Title of host publicationProceedings of the 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005
PublisherIEEE Computer Society
Pages247-252
Number of pages6
ISBN (Print)0780390938, 9780780390935
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005 - Las Vegas, NV, United States
Duration: 15 Aug 200517 Aug 2005

Publication series

NameProceedings of the 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005
Volume2005

Conference

Conference2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005
Country/TerritoryUnited States
CityLas Vegas, NV
Period15/08/0517/08/05

Keywords

  • Ontology merging
  • Schema Matching
  • Semantic Web
  • Text Classification

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