Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Issue title: Special Section: Similarity, correlation and association measures - dedicated to the memory of Lotfi Zadeh
Guest editors: Ildar Batyrshin, Valerie Cross, Vladik Kreinovich and Maria Rifqi
Article type: Research Article
Authors: Sathiya, B.; * | Geetha, T.V.
Affiliations: Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India
Correspondence: [*] Corresponding author. B. Sathiya, Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India. Tel.: +91 9789517984; E-mail: [email protected].
Abstract: Ontologies are the extensively used structural and semantic knowledge representation to describe any entities with non-ambiguous meaning and relations. A large number of general and domain specific semantic similarity measures are available in the literature to access the similarity among these rich knowledge bases. Nevertheless, none of the measures have the best performance in all domains and applications. Each measure uses different strategies and possesses its own pros and cons. Hence, to consolidate the different kinds of measures, its applicability, the similarity and difference among them, the advantages and disadvantages of the measures, a detailed review of different semantic similarity measures has been carried out in this paper. Specifically, a comprehensive and novel classification of the semantic similarity measures exploring different type of ontology information has been presented and each measure is briefed. Further, the open challenges in this field, existing evaluation methodologies and datasets for the semantic similarity measures are also described.
Keywords: Semantic similarity, ontologies, path based measures, depth based measures, IC based measures, DL based measures, feature based measures
DOI: 10.3233/JIFS-18120
Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3045-3059, 2019
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]