Please use this identifier to cite or link to this item:
Title: Ontology-based fuzzy-syllogistic reasoning
Authors: Zarechnev, Mikhail
Kumova, Bora İsmail
Keywords: Approximate reasoning
Categorical syllogisms
Fuzzy inference
Publisher: Springer Verlag
Source: Zarechnev, M., and Kumova, B. İ. (2015). Ontology-based fuzzy-syllogistic reasoning. Lecture Notes in Computer Science, 9101, 179-188. doi:10.1007/978-3-319-19066-2_18
Abstract: We discuss the Fuzzy-Syllogistic System (FSS) that consists of the well-known 256 categorical syllogisms, namely syllogistic moods, and Fuzzy- Syllogistic Reasoning (FSR), which is an implementation of the FSS as one complex approximate reasoning mechanism, in which the 256 moods are interpreted as fuzzy inferences. Here we introduce a sample application of FSR as ontology reasoner. The reasoner can associate up to 256 possible fuzzyinferences with truth ratios in [0,1] for every triple concept relationship of the ontology. We further discuss a transformation technique, by which the truth ratio of a fuzzy-inference can increase, by adapting the fuzzy-quantifiers of a fuzzy-inference to the syllogistic logic of the sample propositions.
Description: 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015; Seoul; South Korea; 10 June 2015 through 12 June 2015
ISBN: 9783319190655
ISSN: 0302-9743
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File Description SizeFormat 
5479.pdfConference Paper358.7 kBAdobe PDFThumbnail
Show full item record

CORE Recommender


checked on Apr 5, 2024

Page view(s)

checked on May 20, 2024


checked on May 20, 2024

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.