Trust, but verify: Predicting contribution quality for knowledge base construction and curation
|Trust, but verify: Predicting contribution quality for knowledge base construction and curation|
|Author(s)||Tan C.H., Agichtein E., Ipeirotis P., Gabrilovich E.|
|Published in||WSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining|
|Keyword(s)||crowdsourcing, knowledge base construction, predicting contribution quality (Extra: Data mining, Information retrieval, Knowledge based systems, Websites, Accuracy rate, Crowdsourcing, Ground truth, Human evaluation, Knowledge base, Knowledge repository, Knowledge-base construction, Wikipedia, Forecasting)|
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Trust, but verify: Predicting contribution quality for knowledge base construction and curation is a 2014 conference paper written in English by Tan C.H., Agichtein E., Ipeirotis P., Gabrilovich E. and published in WSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining.
The largest publicly available knowledge repositories, such as Wikipedia and Freebase, owe their existence and growth to volunteer contributors around the globe. While the majority of contributions are correct, errors can still creep in, due to editors' carelessness, misunderstanding of the schema, malice, or even lack of accepted ground truth. If left undetected, inaccuracies often degrade the experience of users and the performance of applications that rely on these knowledge repositories. We present a new method, CQUAL, for automatically predicting the quality of contributions submitted to a knowledge base. Significantly expanding upon previous work, our method holistically exploits a variety of signals, including the user's domains of expertise as reflected in her prior contribution history, and the historical accuracy rates of different types of facts. In a large-scale human evaluation, our method exhibits precision of 91% at 80% recall. Our model verifies whether a contribution is correct immediately after it is submitted, significantly alleviating the need for post-submission human reviewing.
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