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A Technique for Learning Similarities on Complex Structures with Applications to Extracting Ontologies
The College of Economy and Computer Science.
Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
2005 (English)In: Proceedings of the 3rd Atlantic Web Intelligence Conference (AWIC), Springer , 2005, 991-995 p.Conference paper (Refereed)
Abstract [en]

A general similarity-based algorithm for extracting ontologies from data has been provided in [1]. The algorithm works over arbitrary approximation spaces, modeling notions of similarity and mereological part-of relations (see, e.g., [2, 3, 4, 5]). In the current paper we propose a novel technique of machine learning similarity on tuples on the basis of similarities on attribute domains. The technique reflects intuitions behind tolerance spaces of [6] and similarity spaces of [7]. We illustrate the use of the technique in extracting ontologies from data.

Place, publisher, year, edition, pages
Springer , 2005. 991-995 p.
, Lecture Notes in Computer Science, ISSN 0302-9743 ; 3528
National Category
Computer Science
URN: urn:nbn:se:liu:diva-31859DOI: 10.1007/11495772_29Local ID: 17686OAI: diva2:252682
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2011-02-23

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Szalas, Andrzej
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ReferencesLink to record
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