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Efficient Approximate Entity Matching Using Jaro-Winkler Distance

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Web Information Systems Engineering – WISE 2017 (WISE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10569))

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Abstract

Jaro-Winkler distance is a measurement to measure the similarity between two strings. Since Jaro-Winkler distance performs well in matching personal and entity names, it is widely used in the areas of record linkage, entity linking, information extraction. Given a query string q, Jaro-Winkler distance similarity search finds all strings in a dataset D whose Jaro-Winkler distance similarity with q is no more than a given threshold \(\tau \). With the growth of the dataset size, to efficiently perform Jaro-Winkler distance similarity search becomes challenge problem. In this paper, we propose an index-based method that relies on a filter-and-verify framework to support efficient Jaro-Winkler distance similarity search on a large dataset. We leverage e-variants methods to build the index structure and pigeonhole principle to perform the search. The experiment results clearly demonstrate the efficiency of our methods.

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    https://github.com/AKSW/LIMES-dev.

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Acknowledgement

This work was supported by ARC DP DP130103401 and DP170103710.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Univeristy of New South Wales, Sydney, Australia

    Yaoshu Wang, Jianbin Qin & Wei Wang

Authors
  1. Yaoshu Wang
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  2. Jianbin Qin
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  3. Wei Wang
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Corresponding author

Correspondence to Yaoshu Wang .

Editor information

Editors and Affiliations

  1. University of Sydney, Darlington, NSW, Australia

    Athman Bouguettaya

  2. Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Institute of Computing for Physics and Technology, Protvino, Russia

    Andrey Klimenko

  4. Nanyang Technological University, Singapore, Singapore

    Lu Chen

  5. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Xiangliang Zhang

  6. Institute of Computing for Physics and Technology, Protvino, Russia

    Fedor Dzerzhinskiy

  7. Shanghai Jiao Tong University, Minhang Qu, China

    Weijia Jia

  8. Institute of Computing for Physics and Technology, Protvino, Russia

    Stanislav V. Klimenko

  9. City University of Hong Kong, Kowloon, Hong Kong

    Qing Li

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Wang, Y., Qin, J., Wang, W. (2017). Efficient Approximate Entity Matching Using Jaro-Winkler Distance. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10569. Springer, Cham. https://doi.org/10.1007/978-3-319-68783-4_16

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  • DOI: https://doi.org/10.1007/978-3-319-68783-4_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68782-7

  • Online ISBN: 978-3-319-68783-4

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