Building and Querying Knowledge Graphs from Text Corpora

知识图(KGs)是具有灵活数据模型的知识库，它无缝地表示来自传统数据库的结构化数据和来自文本的半结构化信息。图中的节点是真实世界的实体或其属性，而边将实体连接到属性或其他相关实体。KG有一个本体，描述KG中关系的类型层次结构以及域和范围。在过去的十年里，几个大规模的通用KGS被建立起来，并发现了许多重要的应用：实现语义搜索、问题回答和对接近人类水平的深层神经方法的远程监控。KGs还用于共享链接的开放数据(LOD)云上的知识。虽然KGS的理论基础已经被很好地理解，并且有可靠的KG管理系统，但我们缺乏一个从现有数据集创建相互关联的KG的原则性过程。 这一研究计划将提供原则性的算法和系统，用于从现有的半结构化文档库(混合文本、表格和列表)和用于链接目的的参考本体建立事实的和相互关联的知识库，以及将自然语言的问题翻译成可以从知识库中回答的结构化查询的算法。通过Discovery Grant进行的研究将利用从文本、机器学习和无共享群集上的横向扩展数据管理技术中提取信息的最先进技术。通过Discovery Grant开发的工具将允许领域专家从现有数据集中提取KG，以便他们可以通过结构化查询共享知识。因此，这些工具将有助于决策，在我们生活的现代知识经济中，这需要理解来自结构化数据库的各种数据和不断增加的文本(电子邮件、立法、技术文献等)。此外，通过该计划培训的HQP将获得目前行业需求很高的技能，并将在可预见的未来保持这种水平。最后，通过该计划开发的工具，由于是开放的和基于云的，将允许研究人员和教育工作者跨学科试验并为其领域的幼儿园的发展和他们自己的HQP培训做出贡献。

Knowledge Graphs (KGs) are knowledge bases with a flexible data model that seamlessly represent structured data from traditional databases and semistructured information derived from text. Nodes in the graph are real world entities or their properties while edges connect entities to properties or to other related entities. KGs have an ontology, describing a type hierarchy and the domain and range for the relations in the KG. In the past decade several large-scale and generic KGs were built and found many important applications: enabling semantic search, question answering, and distant-supervision for deep neural methods nearing human-level performance. KGs are also used for sharing knowledge on the Linked Open Data (LOD) cloud. While the theoretical underpinnings of KGs are well understood and there are solid systems for managing KGs, we lack a principled process for creating interlinked KGs from already existing datasets. This research program will contribute principled algorithms and system for building factual and interlinked KGs from an existing corpus of semistructured documents (mixing text, tables and lists) and a reference ontology for interlinking purposes as well as algorithms for translating questions in natural language into structured queries that can be answered from the Kgs. The research carried out through this Discovery Grant will leverage the state-of-the-art in information extraction from text, machine learning, and scale-out data management techniques on shared-nothing clusters. The tools developed through this Discovery Grant will allow domain experts to extract KGs from existing datasets so that they can share that knowledge of make sense of it via structured queries. Thus, these tools will contribute to decision making, which in the modern knowledge economy we live in requires making sense of heterogeneous data coming from structured databases and an ever increasing volume of text (email, legislation, technical literature, et.c). Moreover, the HQP trained through this program will acquire skill that are currently in high demand in industry and will remain so for the foreseeable future. Finally, the tools developed through this program, by virtue of being open and cloud-based, will allow researchers and educators, across disciplines, to experiment with and contribute to the development of KGs in their domain and the training of HQP of their own.