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Author:
Grabowski, Michali ( The College of Economy and Computer Science)
Szalas, Andrzej (Linköping University, The Institute of Technology) (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab)
Title:
A Technique for Learning Similarities on Complex Structures with Applications to Extracting Ontologies
Department:
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab
Linköping University, The Institute of Technology
Publication type:
Conference paper (Refereed)
Language:
English
In:
Proceedings of the 3rd Atlantic Web Intelligence Conference (AWIC)
Publisher: Springer
Series:
Lecture Notes in Computer Science, ISSN 0302-9743; 3528
Pages:
991-995
Year of publ.:
2005
URI:
urn:nbn:se:liu:diva-31859
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-31859
Local ID:
17686
Subject category:
Computer Science
SVEP category:
Computer science
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.

Available from:
2009-10-09
Created:
2009-10-09
Last updated:
2011-02-23
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