Abstract
We propose a latent feature extraction method for record linkage. We first introduce a probabilistic model that generates records with their latent topics. The proposed generative model is designed to utilize the co-occurrence among the attributes of the record. Then, we derive a topic estimation algorithm using the Gibbs sampling technique. The estimated topics are used to identify records. The proposed algorithm works in an unsupervised way; i.e., we do not need to prepare labor-intensive training data. We evaluated the proposed model using bibliographic records and proved that the proposed method tended to perform better for records with more attributes by utilizing their co-occurrence.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bhattacharya, L., Getoor, L.: A Latent Dirichlet Model for Unsupervised Entity Resolution. In: SIAM Intl. Conf. on Data Mining (2006)
Bilenko, M., Mooney, R.J.: Adaptive Duplicate Detection Using Learnable String Similarity Measures. In: International Conference on Knowledge Discovery and Data Mining, pp. 39–48 (2003)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Bollegara, D., Matsuo, Y., Ishizuka, M.: Extracting key phrases to disambiguate personal name queries in web search. In: Coling-ACL Workshop on How Can Computational Liguistics Improve Information Retrieval (2006)
Cohen, W., Ravikumar, P., Fienberg, S.: A Comparison of String Distance Metrics for Name-matching Tasks. In: IIWeb 2003, pp. 73–78 (2003)
Fleischman, M., Hovy, E.: Multi-document Person Name Resolution. In: ACL Workshop on Reference Resolution (2004)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. In: Proc. of the National Academy of Sciences, vol. 101 (suppl. 1), pp. 5228–5235 (2004)
Shu, L., Long, B., Meng, W.: A latent topic model for complete entity resolution. In: Intl. Conf. on Data Engineering, pp. 880–891 (2009)
Song, Y., Huang, J., Councill, I.G., Li, J., Giles, C.L.: Efficient topic-based unsupervised name disambiguation. In: Joint Conference on Digital Libraries, pp. 342–351 (2007)
Wang, X., Mohanty, N., McCallum, A.: Group and topic discovery from relations and text. In: Proc. of 3rd International Workshop on Link Discovery (LinkKDD), pp. 28–35 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Takasu, A., Fukagawa, D., Akutsu, T. (2009). Latent Topic Extraction from Relational Table for Record Matching. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds) Discovery Science. DS 2009. Lecture Notes in Computer Science(), vol 5808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04747-3_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-04747-3_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04746-6
Online ISBN: 978-3-642-04747-3
eBook Packages: Computer ScienceComputer Science (R0)