DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference
DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference
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发表时间：
2012
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通讯作者：
Feng Niu;Ce Zhang;C. Ré;J. Shavlik
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作者：
Feng Niu;Ce Zhang;C. Ré;J. Shavlik
我们提出了一个名为DeepDive的端到端（现场）演示系统，该系统从数亿个网页中执行知识库构建（KBC）。DeepDive采用统计学习和推理来结合不同的数据资源和最佳算法。这种方法的一个关键挑战是可伸缩性，即如何有效地处理tb级的不完美数据。我们描述了如何解决可扩展性挑战，以实现网络规模的KBC，以及我们从构建DeepDive中学到的经验教训。
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 eciently. We describe how we address the scalability challenges to achieve web-scale KBC and the lessons we have learned from building DeepDive.