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DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference
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by
Feng Niu
,
Ce Zhang
,
Christopher Ré
,
Jude Shavlik
| Citations: | 17 - 1 self |
BibTeX
@MISC{Niu_deepdive:web-scale,
author = {Feng Niu and Ce Zhang and Christopher Ré and Jude Shavlik},
title = {DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference},
year = {}
}
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Abstract
We present an end-to-end (live) demonstration system called DeepDive that performs knowledge-base construction (KBC) from hundreds of millions of web pages. DeepDive employs statistical learning and inference to combine diverse data resources and best-of-breed algorithms. A key challenge of this approach is scalability, i.e., how to deal with terabytes of imperfect data efficiently. We describe how we address the scalability challenges to achieve web-scale KBC and the lessons we have learned from building DeepDive. 1.
