Knowledge Acquisition for Expanding Semantic Network

Dariusz Ceglarek

doi:doi:10.11648/j.ijiis.20130202.11

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Knowledge Acquisition for Expanding Semantic Network

Dariusz Ceglarek

Published in International Journal of Intelligent Information Systems (Volume 2, Issue 2)

Published: 2 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.

Published in	International Journal of Intelligent Information Systems (Volume 2, Issue 2)
DOI	10.11648/j.ijiis.20130202.11
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), 2013. 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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[2]	M. Becker, W. Drozdzynski, H.U. Krieger, J. Piskorski, U. Shaafer, and F. Xu: "SProUT -shallow processing with un-ification and typed feature structures", In: Pro¬ceedings of the International Conference on Natural Language Processing, ICON-2002, 2002.
[3]	P. Blackburn and J .Bos: "Representation and Inference for Natural Language", A First Course in Computational Se-mantics, CSLI Publications, 2005.
[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.
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[10]	C. Goddard and A.C. Schalley: "Semantic Analysis", ed. N. Indurkhya F. Damerau In: Handbook of Natural Language Processing, Chapman Hall/CRC, 2010, pp. 93-121.
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[15]	L. Khan, D. McLeod and E. Hovy: "Retrieval effectiveness of an ontology-based model for information selection", 2004.
[16]	R. Krovetz R. and W. B. Croft.: "Lexical Ambiguity and Information Retrieval", 1992.
[17]	W. B. Frakes and R. Baeza-Yates: "Information Retrieval: Data Structures and Algorithms", Prentice Hall, 1992.
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APA Style

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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ACS 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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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://article.sciencepublishinggroup.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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TY  - JOUR
T1  - Knowledge Acquisition for Expanding Semantic Network
AU  - Dariusz Ceglarek
Y1  - 2013/04/02
PY  - 2013
N1  - https://doi.org/10.11648/j.ijiis.20130202.11
DO  - 10.11648/j.ijiis.20130202.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 26
EP  - 33
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20130202.11
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
ER  -

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Author Information

Dariusz Ceglarek

Poznan School of Banking, Poznan, Poland

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APA Style

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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ACS 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

Copy | Download

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://article.sciencepublishinggroup.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}
}

Copy | Download

TY  - JOUR
T1  - Knowledge Acquisition for Expanding Semantic Network
AU  - Dariusz Ceglarek
Y1  - 2013/04/02
PY  - 2013
N1  - https://doi.org/10.11648/j.ijiis.20130202.11
DO  - 10.11648/j.ijiis.20130202.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 26
EP  - 33
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20130202.11
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
ER  -

Copy | Download