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Noise-tolerant approximate blocking for dynamic real-time entity resolution

Liang, H., Wang, Y., Christen, P. and Gayler, R. (2014) Noise-tolerant approximate blocking for dynamic real-time entity resolution. In: The 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 13-16 May 2014, Taiwan, pp. 449-460.

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Entity resolution is the process of identifying records in one or multiple data sources that represent the same real-world entity. This process needs to deal with noisy data that contain for example wrong pronunciation or spelling errors. Many real world applications require rapid responses for entity queries on dynamic datasets. This brings challenges to existing approaches which are mainly aimed at the batch matching of records in static data. Locality sensitive hashing (LSH) is an approximate blocking approach that hashes objects within a certain distance into the same block with high probability. How to make approximate blocking approaches scalable to large datasets and effective for entity resolution in real-time remains an open question. Targeting this problem, we propose a noise-tolerant approximate blocking approach to index records based on their distance ranges using LSH and sorting trees within large sized hash blocks. Experiments conducted on both synthetic and real-world datasets show the effectiveness of the proposed approach.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:82137
Additional Information:Part of the Lecture Notes in Computer Science book series (LNCS, volume 8444). ISBN 9783319066042.


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