Abstract
This paper discusses the automatic concept hierarchy generation process for specific knowledge network. Traditional concept hierarchy generation uses hierarchical clustering to group similar terms, and the result hierarchy is usually not satisfactory for human being recognition. Human-provided knowledge network presents strong semantic features, but this generation process is both labor-intensive and inconsistent under large scale hierarchy. The method proposed in this paper combines the results of specific knowledge network and automatic concept hierarchy generation, which produces a human-readable, semantic-oriented hierarchy. This generation process can efficiently reduce manual classification efforts, which is an exhausting task for human beings. An evaluation method is also proposed in this paper to verify the quality of the result hierarchy.
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Chen, C.-c., Yeh, J.-h., Sie, S.-h.: Government Ontology and Thesaurus Construction: A Taiwan Experience. In: Fox, E.A., Neuhold, E.J., Premsmit, P., Wuwongse, V. (eds.) ICADL 2005. LNCS, vol. 3815, pp. 263–272. Springer, Heidelberg (2005)
Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice-Hall, Englewood Cliffs (1988)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31, 264–323 (1999)
Widyantoro, D., Ioerger, T.R., Yen, J.: An Incremental Approach to Building a Cluster Hierarchy. In: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002) (2002)
Antonio, S.: Organizing topic-specific web information. In: Conference on Hypertext and Hypermedia, Texas, United States, pp. 133–141 (2000)
Chakrabarti, S., Dom, B., Indyk, P.: Enhanced hypertext categorization using hyperlinks. In: Proceedings ACM SIGMOD International Conference on Management of Data, pp. 307–318. ACM Press, Seattle (1998)
Chuang, S.-L., Chien, L.-F.: Automatic query taxonomy generation for information retrieval applications. Online Information Review (OIR) 27(4), 243–255 (2003)
Kashyap, V., Ramakrishnan, C., Rindflesch, T.C.: Towards (Semi-)automatic Generation of Bio-medical Ontologies. In: Poster Proceedings of the AMIA 2003 Annual Symposium, Washington, DC (November 2003)
Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. In: Proceedings of ICML 1997, 14th International Conference on Machine Learning (1997)
Li, F., Yang, Y.: A loss function analysis for classification methods in text categorization. In: The Twentith International Conference on Machine Learning (ICML 2003), pp. 472–479 (2003)
Valdes-Perez, R.E., et al.: Demonstration of Hierarchical Document Clustering of Digital Library Retrieval Results. In: Joint Conference on Digital Libraries (JDCL 2001), Roanoke, VA (presented as a demonstration), June 24-28 (2001)
Yang, Y., Zhang, J., Kisiel, B.: A scalability analysis of classifiers in text categorization. In: ACM SIGIR 2003, pp. 96–103 (2003)
Bhavsar, V., Boley, H., Yang, L.: A Weighted-Tree Similarity Algorithm for Multi-Agent Systems in E-Business Environments. Computational Intelligence Journal 20(4) (2004)
Morrison, T.: Similarity Measure Building for Website Recommendation within an Artificial Immune System, Ph.D. Thesis, University of Nottingham (2003)
Mirkin, B.: Mathematical Classification and Clustering. Kluwer, Dordrecht (1996)
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Yeh, Jh., Sie, Sh. (2006). Towards Automatic Concept Hierarchy Generation for Specific Knowledge Network. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_105
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DOI: https://doi.org/10.1007/11779568_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35453-6
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