A Knowledge Representation Tool for Autonomous Machine Learning Based on Concept Algebra

  • Authors:

  • Yousheng Tian;Yingxu Wang;Kai Hu

  • Affiliations:

  • Theoretical and Empirical Software Engineering Research Centre (TESERC) International Center for Cognitive Informatics (ICfCI) Dept. of Electrical and Computer Engineering Schulich School of Engin ...;Theoretical and Empirical Software Engineering Research Centre (TESERC) International Center for Cognitive Informatics (ICfCI) Dept. of Electrical and Computer Engineering Schulich School of Engin ...;Theoretical and Empirical Software Engineering Research Centre (TESERC) International Center for Cognitive Informatics (ICfCI) Dept. of Electrical and Computer Engineering Schulich School of Engin ...

  • Venue:
  • Transactions on Computational Science V
  • Year:
  • 2009

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Abstract

Concept algebra is an abstract mathematical structure for the formal treatment of concepts and their algebraic relations, operations, and associative rules for composing complex concepts, which provides a denotational mathematic means for knowledge system representation and manipulation. This paper presents an implementation of concept algebra by a set of simulations in Java. A visualized knowledge representation tool for concept algebra is developed, which enables machines learn concepts and knowledge autonomously. A set of eight relational operations and nine compositional operations of concept algebra are implemented in the tool to rigorously manipulate knowledge by concept networks. The knowledge representation tool is capable of presenting concepts and knowledge systems in multiple ways in order to simulate and visualize the dynamic concept networks during machine learning based on concept algebra.