International Journal of Intelligent Information Systems

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Knowledge Acquisition for Expanding Semantic Network

Received: 23 February 2013    Accepted:     Published: 02 April 2013
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Abstract

This article presents the issues of knowledge management, in particular knowledge acquisition. The article summarizes research work started with the SeiPro2S (Semantically Enhanced Intellectual Property Protection System) system designed to protect resources from the unauthorized use of intel¬lectual property. The system implements semantic network as a structure of knowledge repre¬sentation and a new idea of semantic compression. As the author proved that semantic compression is viable concept for English, he decided to focus on potential applications. An algorithm is presented that employ¬ing semantic network WiSENet for knowledge acquisition with flexible rules that yield high precision results. Detailed discussion is given with description of devised algorithm, usage examples and results of experi¬ments.

DOI 10.11648/j.ijiis.20130202.11
Published in International Journal of Intelligent Information Systems (Volume 2, Issue 2, April 2013)
Page(s) 26-33
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Semantic Network, Semantic Compression, WiseNet, Knowledge Acquisition, Lexical Relationships, Natural Language Processing, Knowledge Representation Structures

References
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[4] D. Ceglarek, K. Haniewicz K. and W. Rutkowski: "Seman-tically Enchanced Intellectual Property Protection System - SEIPro2S", 1st International Conference on Computa¬tional Collective Intelligence, Springer Verlag Berlin Heidelberg, 2009, pp. 449—59.
[5] D. Ceglarek, K. Haniewicz and W. Rutkowski: "Semantic compression for specialized Information Retrieval systems", In: Studies in Computational Intelligence, vol. 283, Springer Verlag, Berlin Heidelberg, 2010, pp. 111–121.
[6] D. Ceglarek, K. Haniewicz and W. Rutkowski: "Quality of semantic compression in classification", Lecture Notes in Artificial Intelligence, vol. 6421, Springer-Verlag, Ber-lin-Heidelberg, 2010, pp. 162–171.
[7] D. Ceglarek, K. Haniewicz and W. Rutkowski: "Robust Plagiary Detection Using Semantic Compression Augmented SHAPD", ICCCI 2012 Conference, LNCS, , Springer Verlag, Berlin Heidelberg, 2012, pp. 308–317.
[8] D. Ceglarek: "Architecture of the Semantically Enhanced Intellectual Property Pro¬tection System", In: Lecture Notes in Artificial Intelligence - Computer Recognition System 5, Springer Verlag, Berlin Heidelberg, 2013.
[9] C. Fellbaum: "WordNet - An Electronic Lexical Database", The MIT Press, May 1998.
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[13] A. Hotho, A. Maedche and S. Staab: "Ontology-based Text Document Clustering", In: Pro¬ceedings of the Conference on Intelligent Information Systems, Zakopane, Physica/Springer, 2003.
[14] M. Keikha, N. S. Razavian, F. Oroumchian, H. S. Razi: "Document Representation and Quality of Text: An Analysis", ed.. M. W. Berry M. Castellanos, In: Survey of Text Mining II: Clustering, Classification, and Retrieval, Springer Verlag, Berlin Heidelberg, 2008, pp. 219-232.
[15] L. Khan, D. McLeod and E. Hovy: "Retrieval effectiveness of an ontology-based model for information selection", 2004.
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[17] W. B. Frakes and R. Baeza-Yates: "Information Retrieval: Data Structures and Algorithms", Prentice Hall, 1992.
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Author Information
  • Poznan School of Banking, Poznan, Poland

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    Dariusz Ceglarek. (2013). Knowledge Acquisition for Expanding Semantic Network. International Journal of Intelligent Information Systems, 2(2), 26-33. https://doi.org/10.11648/j.ijiis.20130202.11

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    Dariusz Ceglarek. Knowledge Acquisition for Expanding Semantic Network. Int. J. Intell. Inf. Syst. 2013, 2(2), 26-33. doi: 10.11648/j.ijiis.20130202.11

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    AMA Style

    Dariusz Ceglarek. Knowledge Acquisition for Expanding Semantic Network. Int J Intell Inf Syst. 2013;2(2):26-33. doi: 10.11648/j.ijiis.20130202.11

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  • @article{10.11648/j.ijiis.20130202.11,
      author = {Dariusz Ceglarek},
      title = {Knowledge Acquisition for Expanding Semantic Network},
      journal = {International Journal of Intelligent Information Systems},
      volume = {2},
      number = {2},
      pages = {26-33},
      doi = {10.11648/j.ijiis.20130202.11},
      url = {https://doi.org/10.11648/j.ijiis.20130202.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijiis.20130202.11},
      abstract = {This article presents the issues of knowledge management, in particular knowledge acquisition. The article summarizes research work started with the SeiPro2S (Semantically Enhanced Intellectual Property Protection System) system designed to protect resources from the unauthorized use of intel¬lectual property. The system implements semantic network as a structure of knowledge repre¬sentation and a new idea of semantic compression. As the author proved that semantic compression is viable concept for English, he decided to focus on potential applications. An algorithm is presented that employ¬ing semantic network WiSENet for knowledge acquisition with flexible rules that yield high precision results. Detailed discussion is given with description of devised algorithm, usage examples and results of experi¬ments.},
     year = {2013}
    }
    

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    AU  - Dariusz Ceglarek
    Y1  - 2013/04/02
    PY  - 2013
    N1  - https://doi.org/10.11648/j.ijiis.20130202.11
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    T2  - International Journal of Intelligent Information Systems
    JF  - International Journal of Intelligent Information Systems
    JO  - International Journal of Intelligent Information Systems
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    AB  - This article presents the issues of knowledge management, in particular knowledge acquisition. The article summarizes research work started with the SeiPro2S (Semantically Enhanced Intellectual Property Protection System) system designed to protect resources from the unauthorized use of intel¬lectual property. The system implements semantic network as a structure of knowledge repre¬sentation and a new idea of semantic compression. As the author proved that semantic compression is viable concept for English, he decided to focus on potential applications. An algorithm is presented that employ¬ing semantic network WiSENet for knowledge acquisition with flexible rules that yield high precision results. Detailed discussion is given with description of devised algorithm, usage examples and results of experi¬ments.
    VL  - 2
    IS  - 2
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