Abstract
In many types of databases, such as a science bibliography database, the name attribute is the most commonly used identifier to recognize entities. However, names are frequently ambiguous and not always unique, thereby causing problems in various fields. Name disambiguation is a data management task that aims to properly distinguish different entities that share the same name, particularly for large databases such as digital libraries, because the information that can be used to identify author’s name is limited. In digital libraries, the issue of ambiguous author names occurs due to the existence of multiple authors with the same name or different name variations for the same author. Most previous works conducted to solve this issue frequently used hierarchical clustering approaches based on information within citation records, e.g., co-authors and publication titles. In the present study, we propose a multiple layers name disambiguation framework that is not only applicable to digital libraries but can also be easily extended to other applications. Our framework adopts a dynamic clustering mechanism to minimize clustering errors. We evaluated our approach on real world corpora, and favorable experiment results indicated that our proposed framework was feasible.
Similar content being viewed by others
References
Alvaro, E. & Charles, E. (1997). An efficient domain-independent algorithm for detecting approximately duplicate database records. In Research Issues on Data Mining and Knowledge Discovery, (pp. 23–29).
Amancio, D. R., Oliveira, O. N, Jr., & da Costa, L. F. (2015). Topological-collaborative approach for disambiguating authors names in collaborative networks. Scientometrics, 102(1), 465–485.
Dina, B., & David, J. (1983). Duplicate record elimination in large data files. ACM Transactions on Database Systems, 8(2), 255–265.
Dongwen, L., Byung-Won, O., Jaewoo, K., & Sanghyun, P. (2005). Effective and scalable solutions for mixed and split citation problems in digital libraries. In Proceedings of the 2nd International Workshop on Information Quality in Information Systems. ACM, (pp 69–76).
Han, H., Zhang, H., & Giles, C. L. (2005). Name disambiguation in author citations using a k-way spectral clustering method. In 5th ACM/IEEE Joint Conference on Digital Libraries, (pp. 334–343).
Hanna, P., Bhaskara, M., Brian, M., Stuart, J., & Ilya, S. (2002). Identity uncertainty and citation matching. Neural Information Processing Systems, (pp. 1401–1408).
Hui, H., Hong, Y., & Lee, G. (2005). Name disambiguation in author citations using a k-way spectral clustering method. In 5th ACM/IEEE Joint Conference on Digital Libraries, (pp. 334–343).
Ivan, P., & Alan, B. (1969). A theory for record linkage. Journal of the American Statistical Association, 64(328), 1183–1210.
Kalashnikov, D. V., & Mehrotra, S. (2006). Domain-independent data cleaning via analysis of entity relationship graph. ACM Transactions Database System, 31(2), 716–767.
Liu, Y., Li, W., Huang, Z., & Fang, Q. (2015). A fast method based on multiple clustering for name disambiguation in bibliographic citations. Journal of the Association for Information Science and Technology, 66(3), 636–644.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability.
McCallum, A., Nigam, K., & Ungar, L. H. (2000). Efficient clustering of high-dimensional data sets with application to reference matching. Knowledge Discovery and Data Mining, (pp. 169–178).
Schulz, J. (2015). Using monte carlo simulations to assess the impact of author name disambiguation quality on different bibliometric analyses. Scientometrics, 107(3), 1283–1298.
Shin, D., Kim, T., Choi, J., & Kim, J. (2014). Author name disambiguation using a graph model with node splitting and merging based on bibliographic information. Scientometrics, 100(1), 15–50.
Song, Y., Huang, J., Councill, I. G., Li, J., & Giles., C. L. (2007). Efficient topic-based unsupervised name disambiguation. In 7th ACM/IEEE Joint Conference on Digital Libraries, (pp. 342–352).
Szekely, G. J., & Rizzo, M. L. (2005). Hierarchical clustering via joint between-within distances: Extending ward’s minimum variance method. Journal of Classification, 22, 151–183.
Tang, J., Fong, A., Wang, B., & Zhang, J. (2012). A unified probabilistic framework for name disambiguation in digital library. TKDE, 24(6), 975–987.
Wu, J., & Ding, X. (2013). Author name disambiguation in scientific collaboration and mobility cases. Scientometrics, 96(3), 683–697.
Yang, K. H., Peng, H. T., Jiang, J. Y., Lee, H. M., & Ho, J. M. (2008). Author name disambiguation for citations using topic and web correlation. In Proceedings of 12th European Conference on Research and Advanced Technology for Digital Libraries, (pp. 185–196).
Yin, X. X. & Han, J. W. (2007). Object distinction: Distinguishing objects with identical names. In IEEE 23rd International Conference on Data Engineering, (pp. 1242–1246).
Zhu, J., Fung, G. P. C., & Zhou, X. F. (2009). A term-based driven clustering approach for name disambiguation. Proceedings on Joint APWeb/WAIM, (pp. 320–331).
Zhu, J., Fung, G., & Zhou, X. (2010). Efficient web pages identification for entity resolution. 19th International World Wide Web, (pp. 1223–1224).
Zhu, J., Yang, Y., Xie, Q., Wang, L. W., & Hassan, S. (2014). Robust hybrid name disambiguation framework for large databases. Scientometrics, 98(3), 2255–2274.
Acknowledgements
This work was supported by the National Science Foundation of China (No. 61772211, 61370229, 61750110516), the Natural Science Foundation of Guangdong Province, China (No. 2015A030310509), the S&T Projects of Guangdong Province, China (No. 2016A030303055, 2016B030305004, 2016B010109008), and the science and technology Projects of Guangzhou Municipality, China (201604010003, 201604016019).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhu, J., Wu, X., Lin, X. et al. A novel multiple layers name disambiguation framework for digital libraries using dynamic clustering. Scientometrics 114, 781–794 (2018). https://doi.org/10.1007/s11192-017-2611-8
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-017-2611-8