Name disambiguation in author citations using a K-way spectral clustering method

ABSTRACT

An author may have multiple names and multiple authors may share the same name simply due to name abbreviations, identical names, or name misspellings in publications or bibliographies ¹. This can produce name ambiguity which can affect the performance of document retrieval, web search, and database integration, and may cause improper attribution of credit. Proposed here is an unsupervised learning approach using K-way spectral clustering that disambiguates authors in citations. The approach utilizes three types of citation attributes: co-author names, paper titles, and publication venue titles ². The approach is illustrated with 16 name datasets with citations collected from the DBLP database bibliography and author home pages and shows that name disambiguation can be achieved using these citation attributes.

References

1. Digital bibliography & library project. http://WWW.Informatik.Uni-Trier.DE/ley/db/index.html.
2. Getty's ULAN (Union List of Artist's Names). http://www.getty.edu/research/conducting research/vocabularies/ulan/.
3. The library of congress name authority file. http://www.loc.gov/marc/authority/index.html.
4. Y. Azar, A. Fiat, A. R. Karlin, F. McSherry, and J. Saia. Spectral analysis of data. In Proceedings of 33rd ACM Symposium on Theory of Computing, pages 619--626, 2001.
5. R. Baeza-Yates and B. Ribeiro-Neto. Modern information retrieval. 1999.
6. L. D. Baker and A. K. McCallum. Distributional clustering of words for text classification. In W. B. Croft, A. Moffat, C. J. van Rijsbergen, R. Wilkinson, and J. Zobel, editors, Proceedings of the 21st ACM International Conference on Research and Development in Information Retrieval, pages 96--103, 1998.
7. S. Banerjee and T. Pedersen. An adapted lesk algorithm for word sense disambiguation using wordnet. In Proceedings of the 3rd International Conference on Intelligent Text Processing and Computational Linguistics (CICLING), 2002.
8. Y. Bar-Shalom and T. E. Fortmann. Tracking and Data Association. Academic Press, 1988.
9. M. Bilenko, R. Mooney, W. Cohen, P. Ravikumar, and S. Fienberg. Adaptive name matching in information integration. IEEE Intelligent Systems, 18(5):16--23, 2003.
10. D. Bitton and D. J. DeWitt. Duplicate record elimination in large data files. ACM Transactions on Database Systems, 8(2):255--265, 1983.
11. L. K. Branting. Name-matching algorithms for legal case-management systems. Journal of Information, Law and Technology (JILT), 1, 2002.
12. M. E. Califf and R. J. Mooney. Relational learning of pattern-match rules for information extraction. In Proceedings of the 16th National Conference on Artificial Intelligence, pages 328--334, 1999.
13. W. W. Cohen, H. A. Kautz, and D. A. McAllester. Hardening soft information sources. In Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining, pages 255--259, 2000.
14. I. Dagan, F. C. N. Pereira, and L. Lee. Similarity-based estimation of word cooccurrence probabilities. In Meeting of the Association for Computational Linguistics, pages 272--278, 1994.
15. L. Daniel and J. Slezak. Street talk: the word on address-matching. Business Geographics, pages 26--33, 1995.
16. I. Dhillon, S. Manella, and R. Kumar. A divisive information-theoretic feature clustering for text classification. Journal of Machine Learning Research(JMLR), 3:1265--1287, 2003.
17. T. DiLauro, G. S. Choudhury, M. Patton, J. W. Warner, and E. W. Brown. Automated name authority control and enhanced searching in the levy collection. D-Lib Magazine, 7(4), 2001.
18. W. B. Dolan. Word sense ambiguation: Clustering related senses. Technical report, 1994.
19. P. Drineas, A. Frieze, R. Kannan, S. Vempala, and V. Vinay. Clustering in large graphs and matrices. In SODA: ACM-SIAM Symposium on Discrete Algorithms, pages 291--299, 1999.
20. D. G. Feitelson. On identifying name equivalences in digital libraries. Information Research, 9(4):192, 2004.
21. I. P. Fellegi and A. B. Sunter. A theory for record linkage. Journal of the American Statistical Association, 64:1183--1210, 1969.
22. W. B. Frakes and R. Baeza-Yates. Information Retrieval, Data Structures & Algorithms. Prentice-Hall International (UK) Limited, London, 1992.
23. C. L. Giles, K. Bollacker, and S. Lawrence. CiteSeer: An automatic citation indexing system. In Proceedings of the 3rd ACM Conference on Digital Libraries, pages 89--98, 1998.
24. P. Gillman. National name authority file: Report to the national council on archives. Technical Report British Library Research and Innovation Report 91, The British Library Board, 1998.
25. H. Han, C. L. Giles, E. Manavoglu, H. Zha, Z. Zhang, and E. A. Fox. Automatic document metadata extraction using support vector machines. In Proceedings of the 3rd ACM/IEEE-CS Joint Conference on Digital libraries, pages 37--48, 2003.
26. H. Han, C. L. Giles, H. Zha, C. Li, and K. Tsioutsiouliklis. Two supervised learning approaches for name disambiguation in author citations. In Proceedings of the 4th ACM/IEEE-CS Joint Conference on Digital libraries, 2004.
27. M. A. Hernandez and S. J. Stolfo. Real-world data is dirty: Data cleansing and the merge/purge problem. Data Mining and Knowledge Discovery, 2(1):9--37, 1998.
28. R. Kannan, S. Vempala, and A. Vetta. On clusterings: Good, bad and spectral. In Proceedings of the 41st Foundations of Computer Science, pages 367--380, 2000.
29. J. Karlgren and M. Sahlgren. From words to understanding. In Kanerva et al. (eds.) Foundations of Real World Intelligence. CSLI publications, pages 294--308, 2001.
30. R. Krovetz and W. B. Croft. Word sense disambiguation using machine-readable dictionaries. In Proceedings of the 12th Annual ACMSIGIR Conference, pages 127--136, 1989.
31. M.-L. Lee, T. W. Ling, and W. L. Low. Intelliclean: a knowledge-based intelligent data cleaner. In In 6th International Conference on Knowledge Discovery and Data Mining, pages 290--294, 2000.
32. H. Li and N. Abe. Word clustering and disambiguation based on co-occurrence data. In Proceedings of the 17th Internation Conference on Computational Linguistics, pages 749--755, 1998.
33. D. Lin and P. Pantel. Concept discovery from text. In Proceedings of Conference on Computational Linguistics, pages 577583, 2002.
34. A. McCallum, K. Nigam, and L. H. Ungar. Efficient clustering of high-dimensional data sets with application to reference matching. In Knowledge Discovery and Data Mining, pages 169--178, 2000.
35. A. E. Monge and C. Elkan. An efficient domain-independent algorithm for detecting approximately duplicate database records. In Research Issues on Data Mining and Knowledge Discovery, pages 23--29, 1997.
36. A. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In Proceedings of Advances in Neural Information Processing Systems, pages 849--856, 2001.
37. H. Pasula, B. Marthi, B. Milch, S. Russell, and I. Shpitser. Identity uncertainty and citation matching. In Proceedings of Neural Information Processing Systems: Natural and Synthetic, number 15, 2002.
38. F. C. N. Pereira, N. Tishby, and L. Lee. Distributional clustering of english words. In Meeting of the Association for Computational Linguistics, pages 183--190, 1993.
39. A. Pirkola, J. Toivonen, H. Keskustalo, K. Visala, and K. Jarvelin. Fuzzy translation of cross-lingual spelling variants. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 345--352, 2003.
40. A. Pothen, H. D. Simon, and K.-P. Liou. Partitioning sparse matrices with eigenvectors of graphs. 11:430--452, 1990.
41. K. Seymore, A. McCallum, and R. Rosenfeld. Learning hidden Markov model structure for information extraction. In Proceedings of AAAI 99 Workshop on Machine Learning for Information Extraction, 1999.
42. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888--905, 2000.
43. M. Skounakis, M. Craven, and S. Ray. Hierarchical hidden markov models for information extraction. In Proceedings of the 18th International Joint Conference on Artificial Intelligence, 2003.
44. A. Takasu. Bibliographic attribute extraction from erroneous references based on a statistical model. In Proceedings of the 3rd ACM/IEEE-CS Joint Conference on Digital libraries, pages 49--60, 2003.
45. S. Tejada, C. Knoblock, and S. Minton. Learning domain-independent string transformation weights for high accuracy object identification. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 350--359, 2002.
46. E. L. Terra and C. L. A. Clarke. Frequency estimates for statistical word similarity measures. In M. Hearst and M. Ostendorf, editors, HLT-NAACL 2003: Main Proceedings, pages 244--251, 2003.
47. H. R. Turtle and W. B. Croft. Uncertainty in information retrieval systems. Uncertainty Management in Information Systems, pages 189--224, 1996.
48. J. W. Warner and E. W. Brown. Automated name authority control. In Proceedings of the 1st ACM/IEEE-CS Joint Conference on Digital libraries (JCDL01), 2001.
49. D. Widdows and B. Dorow. A graph model for unsupervised lexical acquisition. In 19th International Conference on Computational Linguistics, pages 1093--1099, Taipei, Taiwan, August 2002.
50. W. Xu, X. Liu, and Y. Gong. Document clustering based on non-negative matrix factorization. In Proceedings of the 26th ACM International Conference on Research and Development in Information Retrieval (SIGIR03), pages 267--273, 2003.
51. Y. Y. Yao, S. Wong, and L. S. Wang. A non-numeric approach to uncertain reasoning. International Journal of General Systems, 23(4):343--359, 1995.
52. H. Zha, C. Ding, M. Gu, X. He, and H. Simon. Spectral relaxation for k-means clustering. In Neural Information Processing Systems (NIPS 2001), pages 1057--1064, 2001.
53. H. Zha, X. He, C. Ding, M. Gu, and H. Simon. Bipartite graph partitioning and data clustering. In Proceedings of ACM CIKM 2001, the 10th International Conference on Information and Knowledge Management, pages 25--32, 2001.

Index Terms

• Information systems

  • Information retrieval