知识图和自然语言处理 (Knowledge Graphs and Natural-Language Processing)

Emergency-relevant data comes in many varieties. It can be high volume and high velocity, and reaction times are critical, calling for efficient and powerful techniques for data analysis and management. Knowledge graphs represent data in a rich, flexible, and uniform way that is well matched with the needs of emergency management. They build on existing standards, resources, techniques, and tools for semantic data and computing. This chapter explains the most important semantic technologies and how they support knowledge graphs. We proceed to discuss their benefits and challenges and give examples of relevant semantic data sources and vocabularies. Natural-language texts -- in particular those collected from social media such as Twitter -- is a type of data source that poses particular analysis challenges. We therefore include an overview of techniques for processing natural-language texts.

翻译：与紧急情况相关的数据来自许多种类,可以是数量大、速度快、反应时间大,需要高效和有力的数据分析和管理技术。知识图表以与应急管理需要相匹配的丰富、灵活和统一的方式代表数据。它们以现有的标准、资源、技术和语义数据和计算工具为基础。本章解释了最重要的语义技术及其支持知识图表的方式。我们着手讨论这些技术的好处和挑战,并举例说明相关的语义数据来源和词汇。自然语言文本,特别是从诸如Twitter等社会媒体收集的文本,是构成特别分析挑战的一种数据来源。因此,我们包含了处理自然语言文本的技术概览。