Empirical study of constructing a knowledge organization system of patent documents using topic modeling

Scientometrics

Volumn 100, Issue 3, 2014, Pages 787-799

  Hu, Zhengyin ^a,b   Fang, Shu ^a   Liang, Tian ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Knowledge organization system; Principal Component Analysis; Term clumping; Text clustering; Topic model

Indexed keywords

EID: 84906081668     PISSN: 01389130     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11192-014-1328-1     Document Type: Article

References (15)

1. Almeida, J., Barbosa, L., Pais, A., & Formosinho, S. (2007). Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering. Chemometrics and Intelligent Laboratory Systems, 87, 208-217.
2. Blei, D. M. (2011). Probabilistic Topic Models. Resource document. Department of Computer Science of Princeton University. http://www. cs. princeton. edu/~blei/kdd-tutorial. pdf. Accessed 6 March 2012.
3. Blei, D. M., Griffiths, T. L., & Jordan, M. I. (2010). The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. Journal of the ACM, 57(2) doi: 10. 1145/1667053. 1667056.
4. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.
5. Dietz, L., & Stewart, A. (2006). Utilize Probabilistic Topic Models to Enrich Knowledge Bases. Resource document. Fraunhofer Integrated Publication and Information Systems Institute (IPSI). ftp. informatik. rwth-aachen. de/Publications/CEUR-WS/Vol-187/25. pdf. Accessed 6 March 2012.
6. Griffiths, T. L., & Steyvers, M. (2004). Finding Scientific Topics. Proceedings of the National Academy of Sciences, 101(suppl. 1), 5228-5235.
7. Hodge, G. (2000). Systems of Knowledge Organization for Digital Libraries: Beyond Traditional Authority Files. Resource document. The Digital Library Federation of Council on Library and Information Resources. http://www. sims. monash. edu/subjects/ims2603/resources/Assignment2Papers/SKOforDigLib. pdf. Accessed 10 November 2012.
8. Ian, D., & Ravi, S. S. (2005). Towards Efficient and Improved Hierarchical Clustering with Instance and Cluster Level Constraints. The CiteSeerX Resources. http://www. cs. albany. edu/~davidson/Publications/hierFinal. pdf. Accessed 22 May 2013.
9. Kleinsorge, R., Willis, J., & Emrick, S. (2007). AMIA 2007 Tutorial T12 UMLS® Overview. Resource document. National Library of Medicine in National Institutes of Health. http://www. nlm. nih. gov/research/umls/presentations/AMIA-2007-T12-final. ppt. Accessed 8 March 2012.
10. Kunal, P., Suju, R., & Joydeep, G. (2006). Automatic Construction of N-ary Tree Based Taxonomies. Data Mining Workshops, 2006. Hong Kong, pp. 75-79.
11. Kvarv, G. S. (2007). Ontology Learning: Suggesting Associations from Text. Master Dissertation. Norwegian University of Science and Technology, pp. 87.
12. McCallum, Kachites. A. (2002). MALLET: A Machine Learning for Language Toolkit. Open Source Software. University of Massachusetts Amherst. http://mallet. cs. umass. edu. Accessed 2 December 2011.
13. Mimno, D. (2011). Machine Learning with MALLET. Resource document. Information Extraction and Synthesis Laboratory, Department of CS UMass, Amherst. http://mallet. cs. umass. edu/mallet-tutorial. pdf. Accessed 2 March 2012.
14. Xue, Q. L., Yan, Q. S., Shi, X. L., & Hai, X. W. (2012). Automatic taxonomy construction from keywords, KDD '12 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, pp. 1433-1441.
15. Zhang, Y., Porter, A. L., & Hu, Z. Y. (2012). An Inductive Method for "Term Clumping": A Case Study on Dye-Sensitized Solar Cells, the International Conference on Innovative Methods for Innovation Management and Policy, Beijing, P. R. China, May 21-25.