Building and Querying Knowledge Graphs from Text Corpora

从文本语料库构建和查询知识图

• 批准号: RGPIN-2018-04270
• 负责人: Barbosa, Denilson
• 金额: $ 1.68万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712832
• 关键词: Building; Querying; Knowledge; Graphs; Text

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.

知识图（KG）是具有灵活数据模型的知识库，可以无缝表示来自传统数据库的结构化数据和源自文本的半结构化信息。图中的节点是现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。知识图谱有一个本体论，描述知识图谱中的类型层次结构以及关系的域和范围。在过去的十年中，建立了一些大规模的通用知识图谱，并发现了许多重要的应用：为接近人类水平的性能的深度神经方法提供语义搜索、问题回答和远程监督。知识图谱还用于在链接开放数据 (LOD) 云上共享知识。虽然知识图谱的理论基础很好理解，并且有可靠的知识图谱管理系统，但我们缺乏从现有数据集创建相互关联的知识图谱的原则流程。 该研究计划将贡献原理性算法和系统，用于从现有的半结构化文档（混合文本、表格和列表）语料库和用于互连目的的参考本体构建事实和相互关联的知识图谱，以及将自然语言问题翻译成可以从知识图谱回答的结构化查询的算法。通过这项发现资助开展的研究将利用最先进的文本信息提取、机器学习以及无共享集群上的横向扩展数据管理技术。通过这项发现资助开发的工具将允许领域专家从现有数据集中提取知识图谱，以便他们可以通过结构化查询共享知识并理解它。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，决策需要理解来自结构化数据库和不断增加的文本量（电子邮件、立法、技术文献等）的异构数据。此外，通过该计划培训的总部人员将获得目前行业急需的技能，并且在可预见的未来仍将如此。最后，通过该计划开发的工具，由于是开放的和基于云的，将允许跨学科的研究人员和教育工作者试验并促进其领域内 KG 的开发以及自己的 HQP 培训。