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KT: Knowledge Technology — The Next Step of Information Technology (IT)

  • Conference paper
Book cover Rough Sets and Knowledge Technology (RSKT 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5589))

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Abstract

We are living in an information technology (IT) era now. Advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data and information. What will be the next step of IT? Many researchers predict that the next step of IT might be Knowledge Technology (KT). KT refers to a fuzzy set of tools enabling better acquisition, representation, organization, exchange and application of information and knowledge.

In this talk, we will address some issues about the development of IT to KT. Some KT related events happened in the past years [1-5], organizations of KT [6-8], and understandings of KT [9-12] will be introduced. One of the most important issues for developing KT, knowledge acquisition and data mining, will be discussed in a new view of translation [13, 14]. Some basic issues of data mining will be analyzed in this view. A new model of data mining, domain-oriented data-driven data mining (3DM), will be proposed [14-17]. The relationship between traditional domain-driven (or user-driven) data mining models [18-20] and our proposed 3DM model will also be analyzed [21]. Some domain-oriented data-driven data mining algorithms for mining such knowledge as default rule [22], decision tree [23], and concept lattice [24] from database will be introduced. The experiment results of these algorithms are also shown to illustrate the efficiency and performance of the knowledge acquired by 3DM data mining algorithms.

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References

  1. http://www.knowledgetechnologies.net

  2. Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.): RSKT 2006. LNCS (LNAI), vol. 4062. Springer, Heidelberg (2006)

    Google Scholar 

  3. Yao, J., Lingras, P., Wu, W.-Z., Szczuka, M.S., Cercone, N.J., Ślȩzak, D. (eds.): RSKT 2007. LNCS (LNAI), vol. 4481. Springer, Heidelberg (2007)

    Google Scholar 

  4. Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.): RSKT 2008. LNCS (LNAI), vol. 5009. Springer, Heidelberg (2008)

    Google Scholar 

  5. Chen, L.T., Wang, G.Y.: Proc. of the 2008 International Forum on Knowledge Technology, IFKT 2008 (Journal of Chongqing University of Posts and Telecommunications (Natural Science edn.), vol. 20(3) (2008)

    Google Scholar 

  6. http://www.lancs.ac.uk/depts/ktru/ktru.htm

  7. http://www.aktors.org/akt/

  8. http://www.eng.ntu.edu.tw/eng/english/department.asp?key=iktrc

  9. Jankowski, A., Skowron, A.: Toward Perception Based Computing: A Rough-Granular Perspective. In: Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Li, K. (eds.) Web Intelligence Meets Brain Informatics. LNCS (LNAI), vol. 4845, pp. 122–142. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Jankowski, A., Skowron, A.: A Wistech Paradigm for Intelligent Systems. In: Peters, J.F., Skowron, A., Düntsch, I., Grzymała-Busse, J.W., Orłowska, E., Polkowski, L. (eds.) Transactions on Rough Sets VI. LNCS, vol. 4374, pp. 94–132. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  11. http://www.knowledgetechnologies.net/proceedings/presentations/belles/donaldbelles.ppt

  12. Zhong, Y.X.: Knowledge Theory and Artificial Intelligence. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS, vol. 4062, pp. 50–56. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  13. Ohsuga, S.: Knowledge Discovery as Translation. In: Lin, T.Y., Ohsuga, S., Liau, C.-J., Hu, X., Tsumoto, S. (eds.) Foundations of Data Mining and Knowledge Discovery. Studies in Computational Intelligence, vol. 6, pp. 3–19. Springer, Heidelberg (2005)

    Google Scholar 

  14. Wang, G.Y., Wang, Y.: Domain-oriented Data-driven Data Mining: a New Understanding for Data Mining. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition) 20(3), 266–271 (2008)

    Google Scholar 

  15. Wang, G.Y.: Introduction to 3DM: Domain-Oriented Data-Driven Data Mining. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS, vol. 5009, pp. 25–26. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  16. Wang, G.Y., Xia, Y.: Domain-oriented Data-driven Data Mining with Application in GIS. In: The 6th Asian Symposium on Geographic Information Systems from a Computer Science and Engineering Viewpoint, ASGIS 2008, Niigata, Japan, pp. 1–4 (2008)

    Google Scholar 

  17. Wang, G.Y.: Domain-Oriented Data-Driven Data Mining (3DM): Simulation of Human Knowledge Understanding. In: Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Li, K. (eds.) Web Intelligence Meets Brain Informatics. LNCS (LNAI), vol. 4845, pp. 278–290. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  18. Zhao, Y., Yao, Y.Y.: Interactive Classification Using a Granule Network. In: Proc. of the 4th IEEE Int. Conf. on Cognitive Informatics, Irvine, USA, pp. 250–259 (2005)

    Google Scholar 

  19. Zhang, C., Cao, L.: Domain-Driven Data Mining: Methodologies and Applications. In: Li, Y.F., Looi, M., Zhong, N. (eds.) Advances in Intelligent IT - Active Media Technology, pp. 13–16 (2006)

    Google Scholar 

  20. Cao, L., Lin, L., Zhang, C.: Domain-driven In-depth Pattern Discovery: A practical methodology, [Research Report], Faculty of Information Technology, University of Technology, Sydney, Australia (2005)

    Google Scholar 

  21. Wang, G.Y., Wang, Y.: 3DM: Domain-oriented Data-driven Data Mining. Fundamenta Informaticae. In: Proc. IEEE Conference on Evolutionary Computation, vol. 90, pp. 1–32 (2009)

    Google Scholar 

  22. Wang, G.Y., He, X.: A Self-Learning Model under Uncertain Condition. Journal of Software 14(6), 1096–1102 (2003)

    MATH  Google Scholar 

  23. Yin, D.S., Wang, G.Y., Wu, Y.: Data-Driven Decision Tree Learning Algorithm Based on Rough Set Theory. In: Tarumi, H., Li, Y., Yoshida, T. (eds.) Proc. of the 2005 International Conference on Active Media Technology, Takamatsu, Kagawa, Japan, pp. 579–584 (2005)

    Google Scholar 

  24. Wang, Y., Wang, G.Y., Deng, W.B.: Concept Lattice Based Data-Driven Uncertain Knowledge Acquisition. Pattern Recognition and Artificial Intelligence 20(5), 636–642 (2007)

    Google Scholar 

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Authors and Affiliations

  1. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R. China

    Guoyin Wang

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  1. Guoyin Wang
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Editor information

Editors and Affiliations

  1. Faculty of Engineering and Surveying and Engineering, University of Southern Queensland, QLD 4350, Australia

    Peng Wen

  2. School of Information Technology, Queensland University of Technology, QLD 4001, Brisbane, Australia

    Yuefeng Li

  3. Institute of Mathematics, Warsaw University of Technology, Koszykowa 86, 02008, Warsaw, Poland

    Lech Polkowski

  4. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Yiyu Yao

  5. Faculty of Medicine, Department of Medical Informatics, Shimane University, 89-1 Enya-cho, Izumo, 693-8501, Shimane, Japan

    Shusaku Tsumoto

  6. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

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© 2009 Springer-Verlag Berlin Heidelberg

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Wang, G. (2009). KT: Knowledge Technology — The Next Step of Information Technology (IT). In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_3

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  • DOI: https://doi.org/10.1007/978-3-642-02962-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02961-5

  • Online ISBN: 978-3-642-02962-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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