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Published in:
2009 | OriginalPaper | Chapter
Latent Topic Extraction from Relational Table for Record Matching
Authors : Atsuhiro Takasu, Daiji Fukagawa, Tatsuya Akutsu
Published in: Discovery Science
Publisher: Springer Berlin Heidelberg
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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.