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Removal of Network Ambiguities through Knowledge based System

by Pradeep Kumar, Sumit Khulbe, H S Dhami
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 14
Year of Publication: 2012
Authors: Pradeep Kumar, Sumit Khulbe, H S Dhami
10.5120/9184-3605

Pradeep Kumar, Sumit Khulbe, H S Dhami . Removal of Network Ambiguities through Knowledge based System. International Journal of Computer Applications. 57, 14 ( November 2012), 31-35. DOI=10.5120/9184-3605

@article{ 10.5120/9184-3605,
author = { Pradeep Kumar, Sumit Khulbe, H S Dhami },
title = { Removal of Network Ambiguities through Knowledge based System },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 14 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 31-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number14/9184-3605/ },
doi = { 10.5120/9184-3605 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:00:27.485568+05:30
%A Pradeep Kumar
%A Sumit Khulbe
%A H S Dhami
%T Removal of Network Ambiguities through Knowledge based System
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 14
%P 31-35
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Documents on the Internet are composed of several kinds of multimedia information when accessed for personal, entertainment, business, and scientific purposes. There are many specific content domains of interest to different communities of users. Extracting semantic relationships between entities from text documents is challenging task in information extraction. By semantics for natural language in this connection, this paper understand not just the relating of a semantic representation language to natural language but the evaluation of natural language expressions with respect to databases. Evaluating a declarative sentence (on a given reading) with respect to a database involves determining whether the sentence is true with respect to the data base, whether the sentence appropriately describes the database. Evaluating a question with respect to a database might determine what information in the database would lead to appropriate answers to the question. The implementation of a knowledge-based system that deals with the Very Large Scale requires the important consideration of several problems, including the complexity of the domain, the nature of information processing, and the automation requirements to this problem is the aim of this work. It addresses the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using support vector machines. This paper have used the base phrase chunking information for relation extraction and have also demonstrated the use of Word Net in feature-based relation extraction to further improve the performance.

References
  1. Barbara Grosz, Karen Soarck Jones and Bonnie Lynn Webber
  2. Readings in Natural Language Processing available in AI SB library.
  3. Barbara Partee, Alice ter Muelen and Robert Wall
  4. , "Mathematical Methods in Linguistics".
  5. Canu S, Grandvalet Y, Rakotomamonjy A (2003). "SVM and Kernel Methods MATLAB Toolbox. " Perception Syst`emes et Information, INSA de Rouen, Rouen, France. URL http://asi. insa-rouen. fr/~arakotom/toolbox/index.
  6. Chang CC, Lin CJ (2001). "libsvm: A Library for Support Vector Machines. " URL http: //www. csie. ntu. edu. tw/~cjlin/libsvm.
  7. Collobert R, Bengio S, Mari´ethoz J (2002). "Torch: A Modular Machine Learning Software Library. " URL http://www. torch. ch/.
  8. D. Skuce, S. Matwin, B. Tauzovich, F. Oppacher, S. Szpakowicz
  9. A logic-based knowledge source system for natural language documents Original Research Article Data & Knowledge Engineering, Volume 1, Issue 3, pp. 201-231.
  10. David Warren and Fernando Pereira
  11. , Computational Linguistics, Vol. 8, Nos 3-4, pp 110-122, An E?cient Easily Adpatable System for Interpreting Natural Language Queries".
  12. Elizabeth DuRoss Liddy
  13. , Anaphora in natural language processing Volume 26, Issue 1, pp. 39-52.
  14. Elisabeth Métais
  15. Enhancing information systems management with natural language processing techniques Data Knowledge Engineering, Volume 41, Issues 2–3, pp. 247-272.
  16. Feather, M. S. , 1987. A survey and classi?cation of some program transformation approaches and techniques. In: Meertens, L. G. L. T. (Ed. ), Program Speci?cation and Transformation. In: IFIP, Elsevier Science Publishers, pp. 165–195.
  17. Gammerman A, Bozanic N, Scholkopf B, Vovk V, Vapnik V, Bottou L, Smola A, Watkins ¨ C, LeCun Y, Saunders C, Stitson M, Weston J (2001). "Royal Holloway Support Vector Machines. " URL http://svm. dcs. rhbnc. ac. uk/dist/index. shtml.
  18. Gunn SR (1998). "MATLAB Support Vector Machines. " University of Southampton, Electronics and Computer Science, URL http://www. isis. ecs. soton. ac. uk/resources/ svminfo/.
  19. GuoDong Zhou, Min Zhang
  20. , Extracting relation information from text documents by exploring various types of knowledge Original Information Processing & Management, Volume 43, Issue 4, pp. 969-982.
  21. Guermeur Y (2004). "M-SVM. " Lorraine Laboratory of IT Research and its Applications, URL http://www. loria. fr/~guermeur/.
  22. Jeremy Clare, Nick Ostler
  23. An approach combining Natural language and knowledge-based systems Volume 26, Issue 2, pp 40-42.
  24. Jose Perez-Carballo, Tomek Strzalkowski
  25. Natural language information retrieval: progress report Information Processing & Management, Volume 36, Issue 1, January 2000, Pages 155-178.
  26. Joachims T (1999). "Making Large-scale SVM Learning Practical. " In "Advances in Kernel Methods – Support Vector Learning," chapter 11. MIT Press. URL http://www- ai. cs. uni-dortmund. de/DOKUMENTE/joachims_99a. ps. gz.
  27. M. Brady and R. Berwick, pp 331-371.
  28. Focusing in the Comprehension of De?nite Anaphora.
  29. Meyer D, Leisch F, Hornik K (2003). "The Support Vector Machine Under Test. " Neurocomputing, Volume 55, pp. 169–186.
  30. P Mart??nez Fernández, A. M Garc??a-Serrano
  31. The role of knowledge-based technology in language applications development Original Research Article Expert Systems with Applications, Volume 19, Issue 1, pp. 31-44.
  32. Partsch, H. , 1990. Speci?cation and Transformation of Programs. A Formal Approach to Software Development. Springer-Verlag.
  33. Ruping S (2004). " ¨ mySVM – A Support Vector Machine. " University of Dortmund, Computer Science, URL http://www-ai. cs. uni- dortmund. de/SOFTWARE/MYSVM/index. html.
  34. Schwaighofer A (2005). "SVM Toolbox for MATLAB. " Intelligent Data Analysis group (IDA), Fraunhofer FIRST, URL http://ida. first. fraunhofer. de/~anton/software. html.
  35. Smaragdakis, Y. , Batory, D. , 2000. Application generators. In: Webster, J. (Ed. ), Encyclopedia of Electrical and Electronics Engineering. John Wiley and Sons.
  36. Sumit Khulbe, Richanshu Sharma and H. S. Dhami (2009), Mathematical formalism and computer program for tense conversion in English Grammar, International Transactions in Mathematical Sciences and Computer; July-December 2009, Volume 2, No. 2, pp. 07-317.
  37. Tomek Strzalkowski [1996 Knowledge based system and Information Processing] Volume 31, Issue 3, pp. 397-417.
  38. Thomas C Rindflesch, Marcelo Fiszman
  39. The interaction of domain knowledge and linguistic structure in natural language processing Volume 36, Issue 6, pp. 462-477.
  40. Yen-Lin Chen, Zeng-Wei Hong, Cheng-Hung Chuang
  41. , Expert Systems with Applications, Volume 39, Issue 1, pp. 494-507.
Index Terms

Computer Science
Information Sciences

Keywords

Computer Aided Design Program transformation Vector machine

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