Abstract
Visualization of Wikipedia categories using Self Organizing Maps shows an overview of categories and their relations, helping to narrow down search domains. Selecting particular neurons this approach enables retrieval of conceptually similar categories. Evaluation of neural activations indicates that they form coherent patterns that may be useful for building user interfaces for navigation over category structures.
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References
Chuttur, M.: Overview of the Technology Acceptance Model: Origins, Developments and Future Directions. Indiana University, Working Papers on Information Systems (2009)
Duch, W., Matykiewicz, P., Pestian, J.: Neurolinguistic Approach to Natural Language Processing with Applications to Medical Text Analysis. Neural Networks 21, 1500–1510 (2008)
Duch, W., Szymǎänski, J.: Semantic Web: Asking the Right Questions. In: Proceedings of the 7th International Conference on Information and Management Sciences, pp. 1–8. California Polytechnic State University Press (2008)
Hulle, M.M.V.: Faithful Representations and Topographic Maps: From Distortion- to Information-based Self-organization. J. Wiley, New York (2001)
Itert, L., Duch, W., Pestian, J.: Influence of a Priori Knowledge on Medical Document Categorization. In: IEEE Symposium on Computational Intelligence and Data Mining, pp. 163–170. IEEE Press, New York (2007)
Kaski, S., Honkela, T., Lagus, K., Kohonen, T.: Websom-Self-organizing Maps of Document Collections. Neurocomputing 21, 101–117 (1998)
Kästner, M., Villmann, T.: Fuzzy Supervised Self-Organizing Map for Semi-supervised Vector Quantization. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 256–265. Springer, Heidelberg (2012)
Kintsch, W.: The Representation of Meaning in Memory. Lawrence Erlbaum, New Jersey (1974)
Kohonen, T., Somervuo, P.: Self-organizing Maps of Symbol Strings. Neurocomputing 21, 19–30 (1998)
Miikkulainen, R.: Subsymbolic Natural Language Processing: An Integrated Model of Scripts, Lexicon, and Memory. MIT Press, Cambridge (1993)
Olszewski, D.: An Experimental Study on Asymmetric Self-Organizing Map. In: Yin, H., Wang, W., Rayward-Smith, V. (eds.) IDEAL 2011. LNCS, vol. 6936, pp. 42–49. Springer, Heidelberg (2011)
Rauber, A., Merkl, D., Dittenbach, M.: The Growing Hierarchical Self-organizing Map: Exploratory Analysis of High-dimensional Data. IEEE Trans. Neural Netw. 13, 1331–1341 (2002)
Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computi. Surv. 34, 1–7 (2002)
Szymański, J.: Mining Relations between Wikipedia Categories. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds.) NDT 2010. CCIS, vol. 88, pp. 248–255. Springer, Heidelberg (2010)
Venkatesh, V., Davis, F.: A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Manage. Sci. 186–204 (2000)
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Szymański, J., Duch, W. (2012). Self Organizing Maps for Visualization of Categories. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_20
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DOI: https://doi.org/10.1007/978-3-642-34475-6_20
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