面向知识库构建的特征工程
Feature Engineering for Knowledge Base Construction
摘要：
知识库构建（KBC）是填充知识库的过程，即，关系数据库以及推理规则，具有从文档和结构化源提取的信息。KBC模糊了信息抽取和信息集成这两个传统数据库问题之间的区别。在过去的几年里，我们的团队一直在与科学合作者建立知识库。使用我们的方法，我们已经建立了知识库，这些知识库具有可比性，有时比人类志愿者构建的知识库质量更好。这些知识库需要专家们花费十年甚至更长的时间来构建，与之相反，我们的许多项目都是由一名研究生构建的。我们的KBC方法基于联合概率推理和学习，但我们不认为推理是灵丹妙药或灵丹妙药：推理是一种工具，使我们能够系统地构建，调试和提高此类系统的质量。此外，推理使我们能够以比传统方法更松散耦合的方式构建这些系统。为了支持这个想法，我们构建了DeepDive系统，它的设计目标是让用户“思考功能-而不是算法。“我们认为DeepDive是一种声明性的，因为它指定了他们想要什么，但没有说明如何获得它。我们描述了我们的方法，重点是特征工程，我们认为这是一个研究不足的问题，相对于它对端到端质量的重要性。
英文摘要：
Knowledge base construction (KBC) is the process of populating a knowledge base, i.e., a relational database together with inference rules, with information extracted from documents and structured sources. KBC blurs the distinction between two traditional database problems, information extraction and information integration. For the last several years, our group has been building knowledge bases with scientific collaborators. Using our approach, we have built knowledge bases that have comparable and sometimes better quality than those constructed by human volunteers. In contrast to these knowledge bases, which took experts a decade or more human years to construct, many of our projects are constructed by a single graduate student. Our approach to KBC is based on joint probabilistic inference and learning, but we do not see inference as either a panacea or a magic bullet: inference is a tool that allows us to be systematic in how we construct, debug, and improve the quality of such systems. In addition, inference allows us to construct these systems in a more loosely coupled way than traditional approaches. To support this idea, we have built the DeepDive system, which has the design goal of letting the user "think about features---not algorithms." We think of DeepDive as declarative in that one specifies what they want but not how to get it. We describe our approach with a focus on feature engineering, which we argue is an understudied problem relative to its importance to end-to-end quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
