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ON LOGICAL CHARACTERISATION OF HUMAN CONCEPT LEARNING BASED ON TERMINOLOGICAL SYSTEMS.

The central focus of this article is the epistemological assumption that knowledge could be generated based on human beings' experiences and over their conceptions of the world. Logical characterisation of human inductive learning over their produced conceptions within terminological systems and pro...

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Published in:Logic & Logical Philosophy Vol. 27; no. 4; pp. 545 - 567
Main Author: Badie, Farshad
Format: Article
Published: Logic & Logical Philosophy Dec2018
Subjects:
Theory of knowledge
Inductive teaching
Terms & phrases
Concept learning
Reasoning
Online Access:View this record in EBSCOhost
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        au: Badie, Farshad
        affil: Center for Linguistics, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark
      su:
        Theory of knowledge
        Inductive teaching
        Terms & phrases
        Concept learning
        Reasoning
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        subj:
          Theory of knowledge
          Inductive teaching
          Terms & phrases
          Concept learning
          Reasoning
      keyword:
        concept
        concept construction
        human concept learning
        logic and cognition
        terminological knowledge
        terminological systems
      ab: The central focus of this article is the epistemological assumption that knowledge could be generated based on human beings' experiences and over their conceptions of the world. Logical characterisation of human inductive learning over their produced conceptions within terminological systems and providing a logical background for theorising over the Human Concept Learning Problem (HCLP) in terminological systems are the main contributions of this research. In order to make a linkage between 'Logic' and 'Cognition', Description Logics (DLs) will be employed to provide a logical description and analysis of actual human inductive reasoning (and learning). This research connects with the topics 'logic & learning', 'cognitive modelling', and 'terminological knowledge representation'.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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