Abstract
Entity resolution (ER) is an important step of data preprocessing. Deep learning based entity resolution is a growing topic in research communities. Considering that record structure is hierarchical: token, attribute, record, we propose a hybrid attention-based network framework for entity resolution. It synthesizes information from different abstract levels of record hierarchy. Systematic attention mechanisms are exploited in several aspects of ER: self-attention for internal dependency capture, inter-attention for alignments, and multi-dimensional weight attention for importance discrimination. Also attribute order is taken into account in ER learning for better similarity representations. Moreover, we tackle ER over low-quality data by hybrid soft token alignments. Extensive experiments on 4 datasets are conducted, and the resultsshow that our approach surpasses existing ER approaches.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China under Grants (62002262, 61672142, 61602103, 62072086, 62072084), and the National Key Research & Development Project under Grant (2018YFB1003404).
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Sun, C., Shen, D. (2021). Entity Resolution with Hybrid Attention-Based Networks. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12682. Springer, Cham. https://doi.org/10.1007/978-3-030-73197-7_37
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DOI: https://doi.org/10.1007/978-3-030-73197-7_37
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