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SEMANTIC ANALYSIS ALGORITHMS FOR KNOWLEDGE WORKERS SUPPORT

Abstract

The paper examines various aspects of text analysis application for knowledge worker’s activity realization. Conclusions are drawn about the relevance and importance of processing the non-structured textual information in order to increase knowledge worker’s efficiency, as well as their awareness in different branches of science. The paper considers the existing algorithms of texts semantic analysis as the sphere of documents topical closeness recognition. At the same time, it contains an example of applying the complex methodology of semantic analysis, which includes LSA and LDA methods together with the Zipf’s Law with the objective to solve a typical knowledge worker’s task. Quantitative identifiers of the efficiency of this methodology are given.

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Category:
Conference activity
Type:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Title of issue:
THE SECOND CONFERENCE ON INNOVATIVE TEACHING METHODS (ITM 2017) strony 180 - 194
Language:
English
Publication year:
2017
Bibliographic description:
Rizun N., Rizun M., Taranenko J.: SEMANTIC ANALYSIS ALGORITHMS FOR KNOWLEDGE WORKERS SUPPORT// THE SECOND CONFERENCE ON INNOVATIVE TEACHING METHODS (ITM 2017)/ Varna: , 2017, s.180-194
Bibliography: test
  1. Bahl L., Baker, J., Jelinek E. & Mercer, R. (1977) Perplexity -a Measure of the Difficulty of Speech Recognition Tasks. In Program, 94th Meeting of the Acoustical Society of America, volume 62, page S63.
  2. Baker, J.C. (1998) A Test of Authorship Based on the Rate at which New Words Enter an Author's Text. Journal Article published 1 Jan 1988 in Literary and Linguistic Computing, volume 3, issue 1, pp. 36-39. open in new tab
  3. Blei, D. M. (2012) Introduction to Probabilistic Topic Models. Comm. ACM 55 (4), April, 2012: pp. 77-84 open in new tab
  4. Blei, D. M., Ng, A. & Jordan, M. (2003) Latent Dirichlet Allocation. Journal of Machine Learning Research, 3: pp. 993-1022. open in new tab
  5. Bose, R. P. J. C. & and van der Aalst, W. M. P. (2009) Context Aware Trace Clustering: Towards Improving Process Mining Results. In SIAM International Conference on Data Mining, pages 401-412. open in new tab
  6. Cantú, F.J. & Ceballos, H.G. (2010) A Multiagent Knowledge and Information Network Approach for Managing Research Assets. Expert Systems with Applications, 37(7), 5272-5284. doi:10.1016/j.eswa.2010.01.012 open in new tab
  7. Cheng, H., Lu, Y. & Sheu, C. (2009) An Ontology-Based Business Intelligence Application in a Financial Knowledge Management System. Expert Systems with Applications, 36, 3614-3622. Doi:10.1016/j.eswa.2008.02.047 open in new tab
  8. Deerwester, S., Dumais, S. T. & Harshman, R. (1990) Indexing by Latent Semantic Analysis. http://lsa.colorado.edu/papers/JASIS.lsi.90.pdf
  9. Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977) Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B., Vol. 39. No 1, pp. 1-38 open in new tab
  10. Dumais, S. T., Furnas, G. W., Landauer, T. K. & Deerwester, S. (1988) Using Latent Semantic Analysis to Improve Information Retrieval. In Proceedings of CHI'88: Conference on Human Factors in Computing, New York: ACM, 281-285 open in new tab
  11. Ghattas, J., Peleg, M., Soffer, P., & Denekamp, Y. (2010) Learning the Context of a Clinical Process. In Business Process Management Workshops, pages 545-556. Springer. open in new tab
  12. Gomez-Perez, J. M., Grobelnik, M., Ruiz, C., Tilly, M., & Warren, P. (2009) Using Task Context to Achieve Effective Information Delivery. In Proceedings of the 1st Workshop on Context, Information and Ontologies, pages 1-6. ACM. open in new tab
  13. Hwang, H.G., Chang, I.C., Chen, F.J. & Wu, S.Y. (2008) Investigation of the Application of KMS for Diseases Classifications: A Study in a Taiwanese Hospital. Expert Systems with Applications, 34(1), 725-733. doi:10.1016/j.eswa.2006.10.018 open in new tab
  14. Li, X., Zhu, Z. & Pan, X. (2010, a) Knowledge Cultivating for Intelligent Decision Making in Small & Middle Businesses. Procedia Computer Science, 1(1), 2479-2488. doi:10.1016/j.procs.2010.04.280 open in new tab
  15. Li, Y., Kramer, M.R., Beulens, A.J.M. &Van Der Vorst, J.G.A.J. (2010, b) A Framework for Early Warning and Proactive Control Systems in Food Supply Chain Networks. Computers in Industry, 61, 852-862. Doi:101.016/j.compind.2010.07.010 open in new tab
  16. Liao, S. (2003) Knowledge Management Technologies and Applications-Literature Review From 1995 to 2002. Expert Systems with Applications, 25, 155-164. doi:10.1016/S0957-4174(03)00043-5 open in new tab
  17. Liao, S.H., Chen, C.M. and Wu, C.H. (2008) Mining Customer Knowledge for Product Line and Brand Extension in Retailing. Expert Systems with Applications, 34(3), 1763- 1776. doi:10.1016/j.eswa.2007.01.036 open in new tab
  18. Liu, D.R. & Lai, C.H. (2011) Mining Group-Based Knowledge Flows for Sharing Task Knowledge. Decision Support Systems,50(2), 370-386. doi:10.1016/j.dss.2010.09.004 open in new tab
  19. McInerney, C. (2002) Knowledge Management and the Dynamic Nature of Knowledge. Journal of the American Society for Information Science and Technology, 53(12), 1009-1018. doi:10.1002/asi.10109 open in new tab
  20. MIGnews.com.ua (2009) Authorship of writers can be learned by a special formula, http://mignews.com.ua/science/nauka/2531956.html open in new tab
  21. Nokel, M. A. & Lukashevich, N.V. (2015) Thematic Models: Adding Bigrams and Accounting Similarities Between Unigrams and Bigrams. Computational methods and programming. Vol. 16, pp. 215-217 open in new tab
  22. Nosenko, S. V, Korolev, I. D. & Poddubny, M.I. (2005) The Method of Automatic Classification Formalized Documents in the System of Electronic Document Management, MKK G06F17 / 30 open in new tab
  23. Rizun, M. (2017a) Maturity Models as the Element of Knowledge Management Development. Materials of the Conference "Current Issues Raised by Young Researchers, X". CreativeTime, Krakow. ISBN 9788363058-71-5. Pp. 293 -298.
  24. Rizun, M. (2017b) Software Solutions for Knowledge Workers. Proceedings of the 2nd International Conference on Information Technologies in Management, Publisher: Rocznik Naukowy Wydziału Zarządzania WSM, http://www.wsmciechanow.edu.pl/rocznik-naukowy/ (in print).
  25. Rizun, N. & Taranenko, Y. (2017) Development of the Algorithm of Polish Language Film Reviews Preprocessing. Proceeding of the 2nd International Conference on Information Technologies in Management, Publisher: Rocznik Naukowy Wydziału Zarządzania WSM, http://www.wsmciechanow.edu.pl/rocznik-naukowy/ (in print).
  26. Rizun, N., Kapłanski, P. & Taranenko, Y. (2016a) Development and Research of the Text Messages Semantic Clustering Methodology. 2016, Third European Network Intelligence Conference, Publisher: ENIC, # 33, pp.180-187. open in new tab
  27. Rizun, N., Kapłanski, P. & Taranenko, Y. (2016b) Method of a Two-Level Text-Meaning Similarity Approximation of the Customers' Opinions. Economic Studies -Scientific Papers. University of Economics in Katowice, Nr. 296/2016, pp.64-85. open in new tab
  28. Silwattananusarn, T. & Tuamsuk, K. (2012) Data Mining and its Applications for Knowledge Management: A Literature Review from 2007 to 2012, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.2, No.5, September 2012, pp.13-24 open in new tab
  29. Stajner, T. & Mladenic, D. (2010) Modeling Knowledge Worker Activity. MLR: Workshop on Applications of Pattern Analysis. Workshop and Conference Proceedings 11 (2010), pp. 127-133.
  30. Vorontsov, K.V. & Potapenko, A.A. (2013) Modifications of the EM-algorithm for probabilistic Thematic modeling // Machine learning and data analysis. Vol. 1. No. 6, pp. 657-686 open in new tab
  31. Wang, F. & Fan, H. (2008) Investigation on Technology Systems for Knowledge Management.IEEE, 1-4. doi:10.1109/WiCom.2008.2716 open in new tab
  32. Wu, W., Lee, Y.T., Tseng, M.L. & Chiang, Y.H. (2010) Data Mining for Exploring Hidden Patterns Between KM and its Performance. Knowledge-Based Systems, 23, 397-401. doi:10.1016/j.knosys.2010.01.014 open in new tab
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