The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
CrowdER: crowdsourcing entity resolution
Abstract
Entity resolution is central to data integration and data cleaning. Algorithmic approaches have been improving in quality, but remain far from perfect. Crowdsourcing platforms offer a more accurate but expensive (and slow) way to bring human insight into the process. Previous work has proposed batching verification tasks for presentation to human workers but even with batching, a human-only approach is infeasible for data sets of even moderate size, due to the large numbers of matches to be tested. Instead, we propose a hybrid human-machine approach in which machines are used to do an initial, coarse pass over all the data, and people are used to verify only the most likely matching pairs. We show that for such a hybrid system, generating the minimum number of verification tasks of a given size is NP-Hard, but we develop a novel two-tiered heuristic approach for creating batched tasks. We describe this method, and present the results of extensive experiments on real data sets using a popular crowdsourcing platform. The experiments show that our hybrid approach achieves both good efficiency and high accuracy compared to machine-only or human-only alternatives.
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Cites
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Crowdsourcing systems on the World-Wide Web
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Quality management on Amazon Mechanical Turk
Ipeirotis, P. G., Provost, F., & Wang, J. (2010) · Proceedings of the ACM SIGKDD Workshop on Human Computation · doi:10.1145/1837885.1837906 · Cited by 973
Self-citationAdaptive duplicate detection using learnable string similarity measures
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Cited by
Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm
Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N. Y., Huang, R., & Zhou, X. (2015) · ACM Computing Surveys · doi:10.1145/2794400 · Cited by 719
Data Cleaning: Overview and Emerging Challenges
Chu, X., Ilyas, I. F., Krishnan, S., & Wang, J. (2016) · Proceedings of the 2016 International Conference on Management of Data · doi:10.1145/2882903.2912574 · Cited by 488
Self-citationTruth inference in crowdsourcing: is the problem solved?
Zheng, Y., Li, G., Li, Y., Shan, C., & Cheng, R. (2017) · Proceedings of the VLDB Endowment · doi:10.14778/3055540.3055547 · Cited by 421
Big data challenge: a data management perspective
Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., & Zhou, X. (2013) · Frontiers of Computer Science · doi:10.1007/s11704-013-3903-7 · Cited by 339
Deep entity matching with pre-trained language models
Li, Y., Li, J., Suhara, Y., Doan, A., & Tan, W.-C. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3421424.3421431 · Cited by 339
Metadata
- Authors
- Wang, J., Kraska, T., Franklin, M. J., & Feng, J.
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2012-07
- Volume
- 5
- Issue
- 11
- Pages
- 1483-1494
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W2056748234
- Cited by
- 507
- References
- 21
- FWCI
- 55.31
- Type
- journal article
- Language
- en
- Open access
- closed
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