A Technique for Learning Similarities on Complex Structures with Applications to Extracting Ontologies
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab
Linköping University, The Institute of Technology
Conference paper (Refereed)
Proceedings of the 3rd Atlantic Web Intelligence Conference (AWIC)
Lecture Notes in Computer Science, ISSN 0302-9743; 3528
A general similarity-based algorithm for extracting ontologies from data has been provided in . 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  and similarity spaces of . We illustrate the use of the technique in extracting ontologies from data.