Robust and Scalable Knowledge Extraction from the Web

一种新的查询、解决问题和决策的模式已经在我们的社会中变得普遍，包括应用计算、数学和统计模型从大量原始数据中推断出可操作的信息。这一范式，通常被称为“大数据分析”，正在推动我们的业务，改变我们的教育系统，并掌握着科学和工程领域下一步突破的关键。尽管对这一模式的希望很高，但仍然存在许多挑战，阻碍了这一承诺成为现实。大多数组织中的绝大多数信息，无论是公共的还是私人的，都是文本形式的(电子邮件、备忘录、报告、新闻文章、立法等)，仍然无法使用当前处理结构化数据的分析工具。至于公开可获得的信息，这些信息与分析越来越相关，情况也是有问题的。尽管存在用于在线编码和共享知识的适当标准，但网络上的绝大多数内容仍然仅使用表示标记(例如，超文本标记语言)来提供。再加上内容的数量不断增加(组织内部和在线)，对从这些来源提取可采取行动的信息的有效和高效工具的需求从未如此迫切。这些技术的应用开始出现，例如谷歌的知识图谱，它被提供来补充常规网络搜索的结果。这项研究的目标是推进从非传统信息资源中提取信息的最新进展，这些资源主要包括文本源和半结构化源(在线知识库和数据库)。我们的目标是：(1)从这些来源中推断潜在的结构；(2)找到来源内部和之间的联系；(3)因此能够对这些来源进行数据分析。我们将首先利用我们在无监督下从文件中提取关系以及对信息提取系统进行系统和大规模评估的工作，这些系统发现了一长串需要改进的机会，然后探索人工智能和自然语言处理中更深层次的模型，以提取和链接数据。

A new mode of inquiry, problem solving, and decision making has become pervasive in our society, consisting of applying computational, mathematical, and statistical models to infer actionable information from massive quantities of raw data. This paradigm, often called "Big Data Analytics", is driving our businesses, changing our educational systems, and holds the key to the next breakthroughs in science and engineering. While the hopes for this paradigm are understandably high, many challenges remain that prevent this promise to become a reality. The vast majority of information in most organizations, both public and private, comes in textual form (email, memos, reports, news articles, legislation, etc), remaining out of reach of current analytics tools which deal with structured data. As for publicly available information, which are increasingly more relevant for analytics, the situation is also problematic. Despite the existence of suitable standards for encoding and sharing knowledge online, the vast majority of content on the Web is still made available using only presentation markup (e.g. HTML). Coupled with the ever increasing volume of content (both internal to organizations and online), the need for effective and efficient tools for extracting actionable information from such sources has never been so critical. Applications of these techniques are starting to emerge, such as Google's knowledge graph which is being offered to complement the results of regular Web searches. The goal of this research is to advance the state-of-the-art in information extraction from non-traditional information resources, including both primarily textual sources and semistructured sources (online knowledge bases and databases). Our goals are: (1) to infer latent structure from these sources; (2) to find linkages within and across sources; (3) to enable data analytics over these sources as a result. We will start by leveraging our work on unsupervised extraction of relations from documents and on the systematic and large-scale evaluation of information extraction systems, which have uncovered a long list of opportunities for improvement, and then explore deeper models in AI and NLP for the purpose of extracting and linking data.