Distill A Knowledge Graph from Unstructured Text via Deep Learning Technologies

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。知识图作为一种有效的知识存储和检索方式，近年来在许多智能系统中得到了广泛的应用，并引起了人们的极大兴趣。人们已经构建和发布了许多知识图谱，如Freebase、Wikidata、DBpedia、ConceptNet和Yago，但这些作品都不能完全满足不同应用的需求，因为我们世界的知识在不断地进化和更新。此外，对于具有数百万甚至数十亿节点和边的大型知识图的构建，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力来获得结构化信息。为了克服这一问题，许多方法试图从非结构化文本自动构建知识图，这些方法大致可以分为以下三类：监督方法、半监督方法和远程监督方法。虽然已有的方法已经取得了一些进展，但仍然有许多问题和挑战有待解决。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，并提取构建知识图所需的结构化信息。这涉及到许多子任务，如实体识别、链接预测和指代消歧，由于语言的困难和微妙以及了解某些事物的空灵瞬息万变的性质(例如，事实和知识不断演变)，每一项任务仍然具有挑战性。我们的目标是开发新的技术，通过利用自然语言处理(NLP)、机器学习以及知识表示和推理的技术来自动构建知识图。例如，这种技术的可用性将允许在KG上使用高度可扩展的查询处理技术对大量文档进行智能搜索。我们的目标是能够在给定非结构化文本语料库的情况下，自动高效地构建高质量的知识图谱，并将人工干预降至最低。这将对各种下游任务有用，例如网页搜索(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)，问题回答(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)，以及推荐系统(例如，帮助在一些电子商务网站，如亚马逊和淘宝向客户推荐更准确和相关的产品项目)。该项目属于EPSRC的人工智能技术、自然语言处理和数据库研究领域，主要是ICT主题。

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.