Skip to main content
SpringerLink
Log in

Establishment of Cognitive Relations Based on Cognitive Informatics

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Cognitive informatics (CI) is an interdisciplinary study on modelling of the brain in terms of knowledge and information processing. In CI, objects/attributes are considered as neurons connected to each other via synapse. The relation represents the synapse in CI. In order to represent new information the brain generates new synapse or relation between the existing neurons. Therefore, the establishment of cognitive relations is essential to represent new information. In order to represent new information, we propose an algorithm which creates cognitive relation between the pair of objects and attributes by using the relational attribute and object method. Further, the cognitive relations between the pair of objects or attributes within the context could be checked with newly defined conditions, i.e. the necessary and sufficient condition. These conditions will evaluate whether the relational object and attribute is adequate to have relations between the pair of objects and attributes. The new information is obtained without increasing the number of neurons in brain. It is achieved by creating cognitive relations between the pair of objects and attributes. The obtained results are beneficial to simulate the intelligence behaviour of brain such as learning and memorizing. Integrating the idea of CI into cognitive relations is a promising and challenging research direction. In this paper, we have discussed it from the aspects of cognitive mechanism, cognitive computing and cognitive process.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

References

  1. Wang Y, Johnston R, Smith M. Cognitive informatics: proc. 1st IEEE International Conference (ICCI’02). Calgary: IEEE CS Press; 2003.

    Google Scholar 

  2. Wang Y. On contemporary denotational mathematics for computational intelligence. Transactions on computational science II. Heidelberg: Springer Berlin; 2008. p. 6–29.

    Google Scholar 

  3. Wille R. Formal concept analysis as mathematical theory of concepts and concept hierarchies. In Formal concept analysis. Springer Berlin Heidelberg. 2005; 1–33.

  4. Wang Y. Towards the synergy of cognitive informatics, neural informatics, brain informatics, and cognitive computing. Cognitive informatics for revealing human cognition: Knowledge manipulations in natural intelligence 2012; 1–19.

  5. Wang Y. The OAR model of neural informatics for internal knowledge representation in the brain. Int J Cogn Inform Nat Intell (IJCINI). 2007;1(3):66–77.

    Article  Google Scholar 

  6. Wang Y, Patel S, Patel D. A layered reference model of the brain (LRMB). IEEE Trans Syst Man Cybern Part C Appl Rev. 2006;36(2):124–33.

    Article  Google Scholar 

  7. Wang Y, editor. Novel approaches in cognitive informatics and natural intelligence. Hershey: IGI Global; 2008.

    Google Scholar 

  8. Wang Y. On system algebra: a denotational mathematical structure for abstract system modeling. Int J Cogn Inform Nat Intell (IJCINI). 2008;2(2):20–43.

    Article  Google Scholar 

  9. Ogiela L, Ogiela MR. Fundamentals of cognitive informatics. Advances in cognitive information systems. Heidelberg: Springer Berlin; 2012. p. 19–49.

    Google Scholar 

  10. Wille R. Formal concept analysis as applied lattice theory. In Proc. of 4th International conference on concept lattices and their applications. (CLA 06) Heidelberg: Springer; 2006. p. 42-67.

  11. Annapurna J, Cherukuri AK. Exploring attributes with domain knowledge in formal concept analysis. CIT J Comput Inf Technol. 2013;21(2):109–23.

    Article  Google Scholar 

  12. Cherukuri AK, Dias SM, Vieira NJ. Knowledge reduction in formal contexts using non-negative matrix factorization. Math Comput Simul. 2015;109:46–63.

    Article  Google Scholar 

  13. Kumar CA, Srinivas S. Concept lattice reduction using fuzzy K-means clustering. Expert Syst Appl. 2010;37(3):2696–704.

    Article  Google Scholar 

  14. Kumar CA. Fuzzy clustering-based formal concept analysis for association rules mining. Appl Artif Intell. 2012;26(3):274–301.

    Article  Google Scholar 

  15. Kumar CA, Ishwarya MS, Loo CK. Formal concept analysis approach to cognitive functionalities of bidirectional associative memory. Biol Inspired Cogn Archit. 2015;12:20–33.

    Google Scholar 

  16. Shivhare R, Kumar CA. On the cognitive process of abstraction. Proc Comput Sci. 2016;89:243–52.

    Article  Google Scholar 

  17. Xu W, Pang J, Luo S. A novel cognitive system model and approach to transformation of information granules. Int J Approx Reason. 2014;55(3):853–66.

    Article  Google Scholar 

  18. Shivhare R, Cherukuri AK. Three-way conceptual approach for cognitive memory functionalities. Int J Mach Learn Cybern. 2017;8(1):21-34.

  19. Yao Y. Three-way decisions and cognitive computing. Cogn Comput. 2016:1–12.

  20. Li J, Mei C, Xu W, Qian Y. Concept learning via granular computing: a cognitive viewpoint. Inf Sci. 2015;298:447–67.

    Article  Google Scholar 

  21. Li J, Huang C, Qi J, Qian Y, Liu W. Three-way cognitive concept learning via multi-granularity. Inform Sci. 2017;378: 244-263.

  22. Xu W, Li W. Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets. IEEE Trans Cybern. 2016;46(2):366–79.

    Article  PubMed  Google Scholar 

  23. Marieb, E. N, Hoehn K. Human anatomy and physiology (2nd ed.). Redwood City: The Benjamin/Cummings Publishing Co., Inc. 1992; 12.

  24. Rosenzmeig MR, Leiman AL, Breedlove SM. Biological psychology: an introduction to behavioral, cognitive, and clinical neuroscience. 2nd ed. Sunderlans: Sinauer Associates, Inc., Psublishers; 1999.

    Google Scholar 

  25. Sternberg RJ. In search of the human mind. 2nd ed. Orlando: Harcourt Brace & Co.; 1998. 1995

    Google Scholar 

  26. Steinbuch K, Piske UA. Learning matrices and their applications. IEEE Trans Electron Comput. 1963;6:846–62.

    Article  Google Scholar 

  27. Singh PK, Cherukuri AK, Li J. Concepts reduction in formal concept analysis with fuzzy setting using shannon entropy. Int J Mach Learn Cybern. 2017;8(1):179-189.

Download references

Acknowledgements

Aswani Kumar Cherukuri and Radhika Shivhare acknowledge the financial support from the Department of Science & Technology, Govt. of India under the grant SR/CSRI/118/2014.

Author information

Authors and Affiliations

  1. School of Information Technology & Engineering, VIT University, Vellore, India

    Radhika Shivhare & Aswani Kumar Cherukuri

  2. School of Mathematics, Kunming University of Science and Technology, Kunming, China

    Jinhai Li

Authors
  1. Radhika Shivhare
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aswani Kumar Cherukuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinhai Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aswani Kumar Cherukuri.

Ethics declarations

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shivhare, R., Cherukuri, A.K. & Li, J. Establishment of Cognitive Relations Based on Cognitive Informatics. Cogn Comput 9, 721–729 (2017). https://doi.org/10.1007/s12559-017-9498-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-017-9498-9

Keywords

Search

Navigation