基于DeepDive的增量式知识库构建
Incremental Knowledge Base Construction Using DeepDive
摘要：
用非结构化信息填充数据库是工业和研究中的一个长期问题，包括提取、清理和集成问题。最近用于此问题的名称包括处理暗数据和知识库构建（KBC）。在这项工作中，我们描述了DeepDive，一个结合数据库和机器学习思想来帮助开发KBC系统的系统，我们提出了使KBC过程更有效的技术。我们观察到，KBC的过程是迭代的，我们开发的技术，逐步产生的推理结果KBC系统。我们提出了两种增量推理方法，分别基于采样和变分技术。我们还研究了这些方法的权衡空间，并开发了一个简单的基于规则的优化器。DeepDive包含了所有这些贡献，我们在五个KBC系统上评估了DeepDive，结果表明它可以将KBC推理任务加速两个数量级，对质量的影响可以忽略不计。
英文摘要：
Populating a database with unstructured information is a long-standing problem in industry and research that encompasses problems of extraction, cleaning, and integration. Recent names used for this problem include dealing with dark data and knowledge base construction (KBC). In this work, we describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems, and we present techniques to make the KBC process more efficient. We observe that the KBC process is iterative, and we develop techniques to incrementally produce inference results for KBC systems. We propose two methods for incremental inference, based respectively on sampling and variational techniques. We also study the tradeoff space of these methods and develop a simple rule-based optimizer. DeepDive includes all of these contributions, and we evaluate DeepDive on five KBC systems, showing that it can speed up KBC inference tasks by up to two orders of magnitude with negligible impact on quality.
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