Abstract
Web tables provide us with high-quality sources of structured data. However, we could not use those valuable tables directly owing to various problems such as conflict data and missing headers. We present CrowdIQ, a scalable platform that integrates crowdsourcing technology for improving the quality of web tables. We design CrowdIQL, which is a declarative language aiming at helping requesters operate tables more exactly and flexibly. Crowdsourcing task is also optimized in this platform by providing candidate items and minimizing useless data, which help requesters to get higher quality tables with less cost.
Cite
CITATION STYLE
Xi, Y., Wang, N., Wu, X., Bao, Y., & Zhou, W. (2017). CrowdIQ: A declarative crowdsourcing platform for improving the quality of web tables. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10367 LNCS, pp. 324–328). Springer Verlag. https://doi.org/10.1007/978-3-319-63564-4_28
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