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.
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© 2009 Springer-Verlag Berlin Heidelberg
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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
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DOI: https://doi.org/10.1007/978-3-642-04747-3_38
Publisher Name: Springer, Berlin, Heidelberg
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