Mining concept similarities for heterogeneous ontologies

Autor(en): Todorov, K.
Geibel, P.
Kühnberger, K.-U. 
Stichwörter: Concept similarity; Heterogeneous ontology; Matching algorithm; Similarity measure; Support vector; Variable selection, Data mining, Ontology
Erscheinungsdatum: 2010
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen: 6171 LNAI
Startseite: 86
Seitenende: 100
Zusammenfassung: 
We consider the problem of discovering pairs of similar concepts, which are part of two given source ontologies, in which each concept node is mapped to a set of instances. The similarity measures we propose are based on learning a classifier for each concept that allows to discriminate the respective concept from the remaining concepts in the same ontology. We present two new measures that are compared experimentally: (1) one based on comparing the sets of support vectors from the learned SVMs and (2) one which considers the list of discriminating variables for each concept. These lists are determined using a novel variable selection approach for the SVM. We compare the performance of the two suggested techniques with two standard approaches (Jaccard similarity and class-means distance). We also present a novel recursive matching algorithm based on concept similarities. © 2010 Springer-Verlag Berlin Heidelberg.
Beschreibung: 
Conference of 10th Industrial Conference on Advances in Data Mining, ICDM 2010 ; Conference Date: 12 July 2010 Through 14 July 2010; Conference Code:81187
ISBN: 9783642143991
ISSN: 03029743
DOI: 10.1007/978-3-642-14400-4_7
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954878036&doi=10.1007%2f978-3-642-14400-4_7&partnerID=40&md5=2866789b406e0fbdd07c714ec6f93d34

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