Building and Querying Knowledge Graphs from Text Corpora

知识图（KGs）是一种具有灵活数据模型的知识库，可以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。图中的节点是真实的世界实体或其属性，而边将实体连接到属性或其他相关实体。KG有一个本体，描述了KG中关系的类型层次结构和域和范围。在过去的十年中，建立了几个大规模和通用的KG，并发现了许多重要的应用：使语义搜索，问答和远程监督接近人类水平的性能的深度神经方法。知识库还用于共享有关链接开放数据（LOD）云的知识。虽然幼儿园的理论基础是很好的理解，并有坚实的系统来管理幼儿园，我们缺乏一个原则性的过程，从现有的数据集创建相互关联的幼儿园。* 本研究计划将贡献原则性算法和系统，用于从现有的半结构化文档语料库（混合文本，表格和列表）和用于互连目的的参考本体中构建事实和互连的KG，以及将自然语言中的问题翻译为可以从KG回答的结构化查询的算法。通过这项发现补助金进行的研究将利用最先进的文本信息提取，机器学习和无共享集群上的横向扩展数据管理技术。通过这项发现补助金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，需要理解来自结构化数据库和不断增加的文本（电子邮件，立法，技术文献等）的异构数据。此外，通过该计划培训的HQP将获得目前行业需求量很大的技能，并将在可预见的未来保持这种状态。最后，通过该计划开发的工具，凭借其开放性和基于云的特性，将允许跨学科的研究人员和教育工作者进行实验，并为他们所在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. **