Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

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Institution Author: search.filters.institutionAuthor.Kumova, Bora İsmailSubject: search.filters.subject.Categorical syllogisms

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  • Conference Object
    Citation - WoS: 3
    Citation - Scopus: 6
    Truth Ratios of Syllogistic Moods
    (Institute of Electrical and Electronics Engineers, 2015) Zarechnev, Mikhail; Kumova, Bora İsmail
    The syllogistic system consists of 256 moods, of which only 24 have been recognized as true. From a set-theoretical point of view, a mood can be represented with three sets and their possible relationships. Three sets can have up to seven sub-sets or spaces. In an earlier work we have used 41 permutations of the spaces, out of which every mood matches an individual number as true or false cases. The truth ratio of a mood is then calculated, by relating the true and false cases with each other. In this work we revise the previously presented properties of the moods and the syllogistic system, this time by using the maximum possible cover, which consists of 96 distinct space permutations. Our results mostly verify our previous findings, like the additional true mood anasoy, the inherently symmetric truth distribution of the moods. Additionally we have revealed some new properties, like the equivalence of some moods, which reduces the system to 136 distinct moods.
  • Conference Object
    Citation - Scopus: 3
    Ontology-Based Fuzzy-Syllogistic Reasoning
    (Springer Verlag, 2015) Zarechnev, Mikhail; Kumova, Bora İsmail
    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.