Knowledge Graphs have emerged as a compelling abstraction for capturing key relationship among the entities of interest to enterprises and for integrating data from heterogeneous sources. JPMorgan Chase (JPMC) is leading this trend by leveraging knowledge graphs across the organization for multiple mission critical applications such as risk assessment, fraud detection, investment advice, etc. A core problem in leveraging a knowledge graph is to link mentions (e.g., company names) that are encountered in textual sources to entities in the knowledge graph. Although several techniques exist for entity linking, they are tuned for entities that exist in Wikipedia, and fail to generalize for the entities that are of interest to an enterprise. In this paper, we propose a novel end-to-end neural entity linking model (JEL) that uses minimal context information and a margin loss to generate entity embeddings, and a Wide & Deep Learning model to match character and semantic information respectively. We show that JEL achieves the state-of-the-art performance to link mentions of company names in financial news with entities in our knowledge graph. We report on our efforts to deploy this model in the company-wide system to generate alerts in response to financial news. The methodology used for JEL is directly applicable and usable by other enterprises who need entity linking solutions for data that are unique to their respective situations.

翻译：知识图谱已成为一种引人注目的抽象方法，用于捕捉企业关注实体间的关键关系并整合异构来源的数据。摩根大通（JPMC）正通过在全组织范围内利用知识图谱支持风险评估、欺诈检测、投资建议等多个关键任务应用，引领这一趋势。利用知识图谱的一个核心问题是将文本来源中遇到的提及（例如公司名称）链接到知识图谱中的实体。尽管存在多种实体链接技术，但这些技术均针对维基百科中存在的实体进行优化，无法泛化适用于企业关注的实体。本文提出了一种新颖的端到端神经实体链接模型（JEL），该模型使用最小上下文信息和边界损失生成实体嵌入，并采用Wide & Deep Learning模型分别匹配字符与语义信息。我们证明JEL在将金融新闻中的公司名称提及链接到知识图谱实体方面达到了最先进的性能。我们报告了在全公司系统中部署该模型以响应金融新闻生成警报的工作。JEL所采用的方法可直接适用于其他需要针对其特有数据进行实体链接解决方案的企业。