Ontology-Based Fuzzy-Syllogistic Reasoning

被引：0
|
作者：
Zarechnev, Mikhail [1 ]
Kumova, Bora I. [1 ]
机构：
[1] Izmir Inst Technol, Dept Comp Engn, TR-35430 Urla Izmir, Turkey
来源：
CURRENT APPROACHES IN APPLIED ARTIFICIAL INTELLIGENCE | 2015年 / 9101卷
关键词：
Categorical syllogisms; Approximate reasoning; Ontologies; Fuzzy-logic;
D O I：
10.1007/978-3-319-19066-2_18
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 fuzzy-inferences 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.
引用
收藏
页码：179 / 188
页数：10
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