Distill A Knowledge Graph from Unstructured Text via Deep Learning Technologies

通过深度学习技术从非结构化文本中提取知识图

• 批准号: 2426711
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2426711
• 关键词: Distill; Knowledge; Graph; Unstructured; Text

Summary: A knowledge graph (KG) is a structured graphical representation of semantic knowledge and relations where nodes in the graph denote the entities and the edges represent the relation between them. As an effective way to store and search knowledge, knowledge graphs have been applied in many intelligent systems and drawn a lot of research interest in recent years. And many knowledge graphs have been constructed and published, such as Freebase, Wikidata, DBpedia, ConceptNet and YAGO, but none of these works could completely fulfil the requirement of different applications in that knowledge from our world keeps continuously evolving and updated. Besides, constructing a knowledge graph, especially for a large one with millions or even billions of nodes and edges, still remains a rather challenging problem as it requires large amounts of expert labour to achieve structured information.To overcome this issue, many approaches have attempted to build KGs automatically from unstructured text, which can broadly be classified under the following three categories: supervised approaches, semi-supervised approaches and distant supervision. Although some progress has been made by existing approaches, there are still lots of problems and challenges remaining unsolved.In this project, our aims are to explore different ways of understanding the unstructured data better and extracting structured information needed for the construction of knowledge graphs. This involves lots of subtasks, such as entity identification, link prediction and referential disambiguation, each of which still remains challenging due to the difficulties and subtleties of language and ethereal transient nature of knowing something (e.g. facts and knowledge are continuously evolving). We aim at developing novel techniques for automated knowledge graph construction by leveraging techniques from Nature Language Processing (NLP), Machine Learning, and Knowledge Representation and Reasoning. The availability of such techniques will allow, for instance, for intelligent search over large bodies of documents using highly scalable query processing technologies over KGs.Our objective is to be able, given a corpus of unstructured text, to automatically and efficiently construct a high-quality knowledge graph with minimized human intervention. This would be useful for a variety of downstream tasks, such as Webs search (e.g. improve the relevance and the quality of search in case of search engines like Google and Bing), question answering (e.g. help better understand natural language queries and give accurate answers in applications like Microsoft Cortana and Apple Siri ), and recommendation systems (e.g. help recommend more accurate and related product items to customers in some e-commerce websites like Amazon and Taobao).This project falls within artificial intelligence technologies, natural language processing and databases research areas of EPSRC primarily within the ICT theme.

总结：知识图（KG）是语义知识和关系的结构化图形表示，其中图中的节点表示实体，边表示它们之间的关系。知识图作为一种存储和检索知识的有效方法，近年来在许多智能系统中得到了广泛的应用，并引起了人们的研究兴趣。虽然已经有很多知识图谱的构建和发布，如Freebase、Wikidata、DBpedia、ConceptNet和YAGO等，但这些作品都不能完全满足不同应用的需求，因为我们的世界中的知识是不断进化和更新的。此外，构建知识图，尤其是拥有数百万甚至数十亿节点和边的大型知识图，仍然是一个相当具有挑战性的问题，因为它需要大量的专家劳动来获得结构化信息。为了克服这个问题，许多方法都试图从非结构化文本中自动构建知识图，这些知识图大致可以分为以下三类：监督方法、半监督方法和远程监督。虽然现有的方法已经取得了一定的进展，但仍然有很多问题和挑战没有解决，在这个项目中，我们的目标是探索不同的方法来更好地理解非结构化数据，并提取所需的结构化信息构建知识图。这涉及许多子任务，例如实体识别，链接预测和指称消歧，由于语言的困难和微妙以及了解事物的短暂性（例如事实和知识不断发展），其中每一个仍然具有挑战性。我们的目标是通过利用自然语言处理（NLP），机器学习和知识表示与推理的技术来开发自动知识图构建的新技术。这些技术的可用性将允许，例如，智能搜索的文件，使用高度可扩展的查询处理技术在KGs.We的目标是能够，给定的语料库的非结构化文本，自动和有效地构建一个高质量的知识图，最大限度地减少人为干预。这对于各种下游任务（如Web搜索）都很有用（例如，在Google和Bing等搜索引擎的情况下，提高搜索的相关性和质量），问答（例如，帮助更好地理解自然语言查询，并在Microsoft Cortana和Apple Siri等应用程序中给出准确的答案），和推荐系统（例如，在亚马逊和淘宝等电子商务网站上，帮助向客户推荐更准确和相关的产品项目）。该项目福尔斯人工智能技术，EPSRC的自然语言处理和数据库研究领域主要在ICT主题内。