Knowledge Acquisition for Expanding Semantic Network
International Journal of Intelligent Information Systems
Volume 2, Issue 2, April 2013, Pages: 26-33
Received: Feb. 23, 2013; Published: Apr. 2, 2013
Views 2575      Downloads 90
Author
Dariusz Ceglarek, Poznan School of Banking, Poznan, Poland
Article Tools
PDF
Follow on us
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.
Keywords
Semantic Network, Semantic Compression, WiseNet, Knowledge Acquisition, Lexical Relationships, Natural Language Processing, Knowledge Representation Structures
To cite this article
Dariusz Ceglarek, Knowledge Acquisition for Expanding Semantic Network, International Journal of Intelligent Information Systems. Vol. 2, No. 2, 2013, pp. 26-33. doi: 10.11648/j.ijiis.20130202.11
References
[1]
R. Baeza-Yates, B. Ribeiro-Neto: "Modern Information Retrieval", ACM Press, Addison-Wesley Longman Publishing Co., New York, 1999.
[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.
[9]
C. Fellbaum: "WordNet - An Electronic Lexical Database", The MIT Press, May 1998.
[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.
[11]
J. Gonzalo et al.: "Indexing with WordNet Synsets can improve Text Retrieval", 1998.
[12]
A. Hotho, S. Staab and S. Stumme: "Explaining Text Clus-tering Results using Semantic Structures", In: Principles of Data Mining and Knowledge Discovery, 7th European Con-ference PKDD 2003, 2003.
[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.
[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.
[18]
R. McNaughton, H. Yamada: "Regular expressions and state graphs for automata", IRE Transactions on Electronic Computers EC-9(1), March 1960, pp. 39–47.
[19]
G. A. Miller: "Wordnet: a lexical database for English", Commun. ACM 38, 1995, pp. 39–41.
[20]
M. E. Califf and R. J. Mooney: "Bottom-up relational learning of pattern matching rules for information extraction". Journal of Mach. Learn. Res. 4, Dec. 2003, pp. 177-210.
[21]
F. Ricceri: "Intellectual Capital and Knowledge Management. Strategic management of knowledge resources", Routledge Francis & Taylor Group, New York 2008
[22]
R. Sinha and R. Mihalcea: "Unsupervised graph-basedword sense disambiguation using measures of word semantic si-milarity", In: ICSC, 2007, pp. 363–369.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186