The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking
Abstract
We tackle the problem of entity linking for large collections of online pages; Our system, ZenCrowd, identifies entities from natural language text using state of the art techniques and automatically connects them to the Linked Open Data cloud. We show how one can take advantage of human intelligence to improve the quality of the links by dynamically generating micro-tasks on an online crowdsourcing platform. We develop a probabilistic framework to make sensible decisions about candidate links and to identify unreliable human workers. We evaluate ZenCrowd in a real deployment and show how a combination of both probabilistic reasoning and crowdsourcing techniques can significantly improve the quality of the links, while limiting the amount of work performed by the crowd.
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Cites
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“For a more in-depth coverage, we refer the interested reader to one of the many overviews on this domain, such as [23].”
BackgroundMethodMaximum Likelihood from Incomplete Data via the EM Algorithm
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Cited by
Truth 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
Crowdsourced Data Management: A Survey
Li, G., Wang, J., Zheng, Y., & Franklin, M. J. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2535242 · Cited by 283
Learning from crowdsourced labeled data: a survey
Zhang, J., Wu, X., & Sheng, V. S. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9491-9 · Cited by 203
Zhai, H., Lingren, T., Deleger, L., Li, Q., Kaiser, M., Stoutenborough, L., & Solti, I. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2426 · Cited by 73
Knowledge Learning With Crowdsourcing: A Brief Review and Systematic Perspective
Zhang, J. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105434 · Cited by 66
Metadata
- Authors
- Demartini, G., Difallah, D. E., & Cudré-Mauroux, P.
- Venue
- Proceedings of the 21st international conference on World Wide Web
- Publisher
- ACM
- Published
- 2012-04-16
- Pages
- 469-478
- OpenAlex
- W2113878109
- Cited by
- 448
- References
- 27
- FWCI
- 27.08
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking. https://app.cochat.ai/papers/10.1145/2187836.2187900
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- Demartini, G., Difallah, D. E., & Cudré-Mauroux, P. (2012). ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking. Proceedings of the 21st international conference on World Wide Web. https://doi.org/10.1145/2187836.2187900
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
