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NeCO: Ontology Alignment using Near-miss Clone Detection

The Semantic Web is an endeavour to enhance the web with the ability to represent knowledge. The knowledge is expressed through what are called ontologies. In order to make ontologies useful, it is important to be able to match the knowledge represented in different ontologies. This task is commonly known as ontology alignment. Ontology alignment has been studied, but it remains an open problem with an annual competition dedicated to measure alignment tools' performance. Many alignment tools are computationally heavy, require training, or are useful in a specific field of study. We propose an ontology alignment method, NeCO, that builds on clone detection techniques to align ontologies. NeCO inherits the clone detection features, and it is light-weight, does not require training, and is useful for any ontology. / Thesis (Master, Computing) -- Queen's University, 2014-01-29 14:38:52.873

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OKQ.1974/8593
Date29 January 2014
CreatorsGeesaman, Paul Louis
ContributorsQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish, English
Detected LanguageEnglish
TypeThesis
RightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
RelationCanadian theses

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