Abstract
Entity Matching (EM) involves identifying and linking the given entities from various sources that pertain to identical real-world entities, serving as a fundamental element in data integration tasks. Such matching is assumed to play a pivotal role in enhancing the accuracy and reliability of downstream tasks in data analytics. Typically, the EM procedure comprises two essential stages: blocking and matching. This study focuses on the blocking phase, particularly the operations of candidate pair generation. Thus, the focal point of this study resides in the exploration of different techniques for generating candidate pairings from the sources during the blocking phase. The proposed work is evaluated by experiment on the benchmark datasets, which are DBLP-ACM and Amazon-GoogleProducts.
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Acknowledgements
We are most thankful for the Faculty of Engineering, Chiang Mai University, for supporting us in this study. Additionally, we extend our sincere appreciation to the Chiang Mai University Presidential Scholarship for their financial support, which greatly contributed to the successful completion of this study.
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Chantaranimi, K., Natwichai, J. (2024). Evaluation of Candidate Pair Generation Strategies in Entity Matching. In: Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing . 3PGCIC 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 189. Springer, Cham. https://doi.org/10.1007/978-3-031-46970-1_11
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