Ontological and fuzzy set similarity between perception-based words

Cross, V, Morenko, V, Crockett, K ORCID: https://orcid.org/0000-0003-1941-6201 and Adel, N ORCID: https://orcid.org/0000-0003-4449-7410 (2019) Ontological and fuzzy set similarity between perception-based words. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2019, 23 June 2019 - 26 June 2019, New Orleans, USA.

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Official URL: https://ieeexplore.ieee.org/document/8858947

Abstract

Fuzzy short text semantic similarity measures allow the inclusion of human perception based words to be within the similarity measurement which results in better correlation on the meaning of the short text with human understanding. Existing measures such as FUSE and FAST rely on the creation of fuzzy ontological structures from the modelling of perception words using type-1 or type-2 fuzzy sets. Due to the complex methodology of creating these ontologies, fuzzy word representation cannot be guaranteed due to language evolution. This paper presents a comparative study of simpler fuzzy set similarity measures. The results surprisingly indicate that a very simple fuzzy set similarity measure created from the center of gravity (COG) distance between type-2 fuzzy sets has a very high correlation with the FUSE semantic similarity measure.

Item Type:	Conference or Workshop Item (Paper)
Published Proceedings:	2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Peer-reviewed:	Yes
Date Deposited:	29 Jul 2019 10:48
Publisher:	IEEE
Additional Information:	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Faculties > Science and Engineering
Subject terms:	Perception, similarity measure, fuzzy systems
URI:	https://e-space.mmu.ac.uk/id/eprint/622780
DOI:	https://doi.org/10.1109/FUZZ-IEEE.2019.8858947
ISSN	1544-5615
e-ISSN	1558-4739

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