Abstract
The paper concerns the problem of automatic category system creation for a set of documents connected with references. Presented approach has been evaluated on the Polish Wikipedia, where two graphs: the Wikipedia category graph and article graph has been analyzed. The linkages between Wikipedia articles has been used to create a new category graph with weighted edges. We compare the created category graph with the original Wikipedia category graph, testing its quality in terms of coverage.
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References
Viegas, F., Wattenberg, M., Kriss, J., Van Ham, F.: Talk before you type: Coordination in Wikipedia. In: Hawaii International Conference on System Sciences, vol. 40, p. 1298. IEEE, Los Alamitos (2007)
Voss, J.: Measuring wikipedia. In: Proceedings 10th International Conference of the International Society for Scientometrics and Informetrics (2005)
Buscaldi, D., Rosso, P.: Mining knowledge from wikipedia for the question answering task. In: Proceedings of the International Conference on Language Resources and Evaluation (2006)
Tan, A.: Text mining: The state of the art and the challenges. In: Proceedings of the PAKDD 1999 Workshop on Knowledge Disocovery from Advanced Databases, Citeseer, pp. 65–70 (1999)
Zesch, T., Gurevych, I.: Analysis of the wikipedia category graph for NLP applications. In: Proc. of NAACL-HLT 2007 Workshop: TextGraphs, vol. 2 (2007)
Ponzetto, S., Strube, M.: Deriving a large scale taxonomy from Wikipedia. In: Proceedings of The National Conference on Artificial Intelligence, vol. 22, p. 1440. AAAI Press/MIT Press, Menlo Park/Cambridge (1999/2007)
Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining, Citeseer, vol. 400, pp. 525–526 (2000)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys (CSUR) 34, 1–47 (2002)
Majewski, P., Szymaski, J.: Text categorisation with semantic common sense knowledge: first results. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds.) ICONIP 2007, Part II. LNCS, vol. 4985, pp. 769–778. Springer, Heidelberg (2008)
Milne, D.: Computing semantic relatedness using wikipedia link structure. In: Proceedings of the New Zealand Computer Science Research Student Conference (NZCSRSC 2007), Hamilton, New Zealand (2007)
Hao, P., Chiang, J., Tu, Y.: Hierarchically SVM classification based on support vector clustering method and its application to document categorization. Expert Systems with Applications 33, 627–635 (2007)
Szymaski, J.: Developing WordNet in Wikipedia-like style. In: Proceedings of the 5th International Conference of the Global WordNet Association, pp. 342–347 (2010)
Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 6–12 (2007)
Miller, G.A., Beckitch, R., Fellbaum, C., Gross, D., Miller, K.: Introduction to WordNet: An On-line Lexical Database. Cognitive Science Laboratory, Princeton University Press, Princeton (1993)
Szymaski, J., Kilanowski, D.: Wikipedia and WordNet integration based on words co-occurrences. In: Proceedings of 30th International Conference Information Systems, Architecture and Technology, vol. 1, pp. 93–103 (2009)
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Szymański, J. (2010). Mining Relations between Wikipedia Categories. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds) Networked Digital Technologies. NDT 2010. Communications in Computer and Information Science, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14306-9_25
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DOI: https://doi.org/10.1007/978-3-642-14306-9_25
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