Skip to main content
Springer Nature Link
Log in

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

  • Chapter
Transactions on Computational Science V

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 5540))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Anderson, J.R.: The Architecture of Cognition. Harvard Univ. Press, Cambridge (1983)

    Google Scholar 

  2. Davis, R., Shrobe, H., Szolovits, P.: What is a Knowledge Representation. AI Magazine 14(1), 17–33 (1993)

    Google Scholar 

  3. Ganter, B., Wille, R.: Formal Concept Analysis. Springer, Berlin (1999)

    Book  MATH  Google Scholar 

  4. Glasgow, J., Narayanan, N.H., Chandrasekaran, B.: Diagrammatic Reasoning: Cognitive and Computational Perspectives. AAAI Press, Menlo Park (1995)

    Google Scholar 

  5. Hampton, J.A.: Psychological Representation of Concepts of Memory, pp. 81–110. Psychology Press, Hove (1997)

    Google Scholar 

  6. Hurley, P.J.: A Concise Introduction to Logic, 6th edn. Wadsworth Pub. Co., ITP (1997)

    MATH  Google Scholar 

  7. Matlin, M.W.: Cognition, 4th edn. Harcourt Brace College Pub., NY (1998)

    Google Scholar 

  8. Medin, D.L., Shoben, E.J.: Context and Structure in Conceptual Combination. Cognitive Psychology 20, 158–190 (1988)

    Article  Google Scholar 

  9. Minsky, M.: A Framework for Representing Knowledge, MIT-AI Laboratory Memo 306 (1974)

    Google Scholar 

  10. Sowa, J.F.: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks/Cole, Pacific Grove (2000)

    Google Scholar 

  11. Thomason, R.: Logic and artificial intelligence. The Stanford Encyclopaedia of Philosophy (2003)

    Google Scholar 

  12. Wang, Y.: Keynote: On Cognitive Informatics. In: Proc. 1st IEEE International Conference on Cognitive Informatics (ICCI 2002), Calgary, Canada, pp. 34–42. IEEE CS Press, Los Alamitos (2002)

    Chapter  Google Scholar 

  13. Wang, Y.: On Cognitive Informatics. Brain and Mind: A Transdisciplinary Journal of Neuroscience and Neorophilosophy 4(3), 151–167 (2003)

    Article  MathSciNet  Google Scholar 

  14. Wang, Y.: Keynote: Cognitive Informatics - Towards the Future Generation Computers that Think and Feel. In: Proc. 5th IEEE International Conference on Cognitive Informatics (ICCI 2006), Beijing, China, pp. 3–7. IEEE CS Press, Los Alamitos (2006)

    Chapter  Google Scholar 

  15. Wang, Y.: Software Engineering Foundations: A Software Science Perspective. CRC Book Series in Software Engineering, vol. II. Aurebach Publications, NY (2007a)

    Book  MATH  Google Scholar 

  16. Wang, Y.: The Theoretical Framework of Cognitive Informatics. International Journal of Cognitive Informatics and Natural Intelligence 1(1), 1–27 (2007b)

    Article  MathSciNet  Google Scholar 

  17. Wang, Y.: The OAR Model of Neural Informatics for Internal Knowledge Representation in the Brain. International Journal of Cognitive Informatics and Natural Intelligence 1(3), 64–75 (2007c)

    Google Scholar 

  18. Wang, Y.: On Contemporary Denotational Mathematics for Computational Intelligence. Transactions on Computational Science 2, 6–29 (2008a)

    MATH  Google Scholar 

  19. Wang, Y.: On Concept Algebra: A Denotational Mathematical Structure for Knowledge and Software Modeling. International Journal of Cognitive Informatics and Natural Intelligence 2(2), 1–19 (2008b)

    Article  Google Scholar 

  20. Wang, Y.: Mathematical Laws of Software. Transactions of Computational Science 2, 46–83 (2008c)

    MATH  Google Scholar 

  21. Wang, Y.: On Abstract Intelligence: Toward a Unified Theory of Natural, Artificial, Machinable, and Computational Intelligence. International Journal of Software Science and Computational Intelligence 1(1), 1–17 (2009a)

    Article  MathSciNet  Google Scholar 

  22. Wang, Y.: Towards a Formal Knowledge System Theory and Its Cognitive Informatics Foundations. Transactions of Computational Science 5 (2009b) (to appear)

    Google Scholar 

  23. Westen, D.: Psychology: Mind, Brain, and Culture, 2nd edn. John Wiley & Sons, Inc., NY (1999)

    Google Scholar 

  24. Wilson, R.A., Keil, F.C.: The MIT Encyclopedia of the Cognitive Sciences. MIT Press, Cambridge (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Theoretical and Empirical Software Engineering Research Centre (TESERC) International Center for Cognitive Informatics (ICfCI) Dept. of Electrical and Computer Engineering Schulich School of Engineering, University of Calgary, 2500 University Drive, NW, Calgary, Alberta, Canada, T2N 1N4

    Yousheng Tian, Yingxu Wang & Kai Hu

Authors
  1. Yousheng Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yingxu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kai Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Calgary, Department of Computer Science, 2500 University Drive N.W, T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  2. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  3. Theoretical and Empirical Software Engineering Research Centre (TESERC), University of Calgary, 2500 University Drive, NW, T2N 1N4, Calgary, Alberta, Canada

    Yingxu Wang

  4. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Keith C. C. Chan

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Tian, Y., Wang, Y., Hu, K. (2009). A Knowledge Representation Tool for Autonomous Machine Learning Based on Concept Algebra. In: Gavrilova, M.L., Tan, C.J.K., Wang, Y., Chan, K.C.C. (eds) Transactions on Computational Science V. Lecture Notes in Computer Science, vol 5540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02097-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02097-1_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02096-4

  • Online ISBN: 978-3-642-02097-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics