Intelligence analysts have long struggled with an abundance of data that must be investigated on a daily basis. In the U.S. Army, this activity involves reconciling information from various sources, a process that has been automated to a certain extent, but which remains highly manual. To promote automation, a semantic analysis prototype was designed to aid in the intelligence analysis process. This tool, called Selective Plotting of Interconnected Data and Entity Relations (SPIDER), extracts entities and their relationships from text in order to streamline investigations. SPIDER is a web application that can be remotely-accessed via a web browser, and has three major components: (1) a Java API that reads documents, extracts entities and relationships using Stanford CoreNLP, (2) a Neo4j graph database that stores entities, relationships, and properties; (3) a JavaScript-based SigmaJS visualization tool for displaying the graph on the browser. SPIDER can scale document analysis to thousands of files for quick visualization, making the intelligence analysis process more efficient, and allowing military leadership quicker insights into a vast array of potentially-hidden knowledge.

中文翻译：

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SPIDER：互连数据和实体关系的选择性绘图

情报分析师长期以来一直在努力应对每天必须调查的大量数据。在美国陆军中，这项活动涉及协调来自不同来源的信息，这一过程在一定程度上已实现自动化，但仍然高度手动。为了促进自动化，设计了语义分析原型来帮助情报分析过程。该工具称为互连数据和实体关系的选择性绘图 (SPIDER)，可从文本中提取实体及其关系以简化调查。SPIDER 是一个可以通过 Web 浏览器远程访问的 Web 应用程序，它具有三个主要组件：(1) 一个 Java API，它使用斯坦福 CoreNLP 读取文档、提取实体和关系，(2) 一个存储实体的 Neo4j 图数据库, 关系, 和属性；(3) 基于 JavaScript 的 SigmaJS 可视化工具，用于在浏览器上显示图形。SPIDER 可以将文档分析扩展到数千个文件以进行快速可视化，从而提高情报分析过程的效率，并使军事领导层能够更快地洞察大量潜在隐藏的知识。