Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5479
Title: Ontology-based fuzzy-syllogistic reasoning
Authors: Zarechnev, Mikhail
Kumova, Bora İsmail
Kumova, Bora İsmail
Keywords: Approximate reasoning
Categorical syllogisms
Ontologies
Fuzzy inference
Issue Date: 2015
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
URI: http://doi.org/10.1007/978-3-319-19066-2_18
http://hdl.handle.net/11147/5479
ISBN: 9783319190655
ISSN: 0302-9743
1611-3349
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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