Noise-tolerant approximate blocking for dynamic real-time entity resolutionLiang, 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.
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. Official URL: https://doi.org/10.1007/978-3-319-06605-9_37 Abstract/SummaryEntity 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.
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