Abstract
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.
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Grabowski, M., Szałas, A. (2005). A Technique for Learning Similarities on Complex Structures with Applications to Extracting Ontologies. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds) Advances in Web Intelligence. AWIC 2005. Lecture Notes in Computer Science(), vol 3528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11495772_29
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DOI: https://doi.org/10.1007/11495772_29
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