Abstract
Among the large number of applications of the self-organizing map (SOM) algorithm, creating maps of document collections have become commonplace since the introduction of the WEBSOM system. This article presents a novel development in WEBSOM research. The Interactive Two-Level WEBSOM, I2WEBSOM, includes two main components, a map of terms, and a dynamic map of documents. The map of terms is used to enable interactive feature selection and weighting. The map of documents is calculated using terminology-based feature vectors where their weights can be changed using the first-level map. In the experimental part, we focus on the application of creating maps of people based on their interest or competence profiles.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
- Human Resource Management
- Document Collection
- Latent Semantic Analysis
- Vector Space Model
- Organizational Exploration
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Honkela, T., Kaski, S., Lagus, K., Kohonen, T.: Newsgroup exploration with WEBSOM method and browsing interface. Technical Report A32, Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland (1996)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (2001)
Venna, J., Kaski, S.: Local multidimensional scaling. Neural Networks 19(6), 889–899 (2006)
Ritter, H., Kohonen, T.: Self-organizing semantic maps. Biological Cybernetics (1989)
Harris, Z.: Distributional structure. Word 10(23), 146–162 (1954)
Turney, P.D., Pantel, P.: From frequency to meaning: Vector space models of semantics. J. of Artificial Intelligence Research 37, 141–188 (2010)
Honkela, T., Pulkki, V., Kohonen, T.: Contextual relations of words in Grimm tales analyzed by self-organizing map. In: Proc. of ICANN 1995, Paris, EC2 et Cie, vol. 2, pp. 3–7 (1995)
Honkela, T., Vepsäläinen, A.M.: Interpreting imprecise expressions: Experiments with Kohonen’s self-organizing maps and associative memory. In: Proc. of ICANN 1991, vol. 1, pp. 897–902 (1991)
Lin, X., Soergel, D., Marchionini, G.: A self-organizing semantic map for information retrieval. In: Proc. of the 14th ACM SIGIR, pp. 262–269 (1991)
Jäppinen, H., Honkela, T., Hyötyniemi, H., Lehtola, A.: A multilevel natural language processing model. Nordic Journal of Linguistics 11, 69–82 (1988)
Alkula, R., Honkela, T.: Development of text storage and information retrieval methods with natural language processing components. Final report of the FULLTEXT project (in Finnish). VTT, Espoo, Finland (1992)
Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. J. of the American Society of Information Science 41, 391–407 (1990)
Kaski, S., Honkela, T., Lagus, K., Kohonen, T.: WEBSOM–self-organizing maps of document collections. Neurocomputing 21(1), 101–117 (1998)
Kohonen, T., Kaski, S., Lagus, K., Salojärvi, J., Honkela, J., Paatero, V., Saarela, A.: Self organization of a massive document collection. IEEE Transactions on Neural Networks 11(3), 574–585 (2000)
Ong, T.H., Chen, H., Sung, W.K., Zhu, B.: Newsmap: a knowledge map for online news. Decision Support Systems 39(4), 583–597 (2005)
Saarikoski, J., Laurikkala, J., Järvelin, K., Juhola, M.: A study of the use of self-organising maps in information retrieval. Journal of Documentation 65(2), 304–322 (2009)
Ding, Y., Fu, X.: The research of text mining based on self-organizing maps. Procedia Engineering 29, 537–541 (2012)
Lagus, K.: Map of WSOM 1997 abstracts–alternative index. In: Proc. of WSOM 1997, vol. 97, pp. 4–6 (1997)
Honkela, T., Nordfors, R., Tuuli, R.: Document maps for competence management. In: Proc. of the Symposium on Professional Practice in AI, pp. 31–39 (2004)
Piazza, F., Strohmeier, S.: Domain-driven data mining in human resource management: A review. In: Proc. of ICDMW 2011, pp. 458–465 (2011)
Janasik, N., Honkela, T., Bruun, H.: Text mining in qualitative research application of an unsupervised learning method. Organizational Research Methods 12(3), 436–460 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Honkela, T., Knapek, M. (2013). Interactive Two-Level WEBSOM for Organizational Exploration. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_72
Download citation
DOI: https://doi.org/10.1007/978-3-642-40728-4_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40727-7
Online ISBN: 978-3-642-40728-4
eBook Packages: Computer ScienceComputer Science (R0)