标题： 查询知识图谱时的信号作为首要公民 显示英文标题

标题： Signals as a First-Class Citizen When Querying Knowledge Graphs

摘要： 网络物理系统（CPSs）紧密集成了计算与物理实体，通常从成千上万个传感器生成海量的时间序列数据。尽管知识图谱提供了一种强大的手段来为这些数据上下文化，但现有的将知识图谱与时间序列数据集成的方法缺乏一种概念来建模CPSs固有的连续时间值。这一差距可能使对传感器数据的计算表达变得笨拙。在这项工作中，我们提出了知识图谱和信号的集成，这是一个用于建模时间值的成熟概念。通过将信号视为查询语言中的第一类公民，我们可以实现知识图谱和信号之间的无缝查询。知识图谱捕捉CPS的信息，而信号则表示来自传感器的运行时数据。我们讨论了这种方法的影响，并提出SigSPARQL，这是对SPARQL查询语言的扩展，以展示这些概念。此外，我们使用原型评估了实现SigSPARQL的可行性，并展示了查询语言在一个CPS监控用例中的适用性。 显示英文摘要

摘要： Cyber-Physical Systems (CPSs) tightly integrate computation with physical entities, often generating vast amounts of time series data from thousands of sensors. Although knowledge graphs offer a powerful means to contextualize these data, existing approaches to integrating knowledge graphs with time series data lack a concept to model the continuous temporal values inherent in CPSs. This gap can make expressing computations on the sensor data cumbersome. In this work, we propose the integration of knowledge graphs and signals, a proven concept for modeling temporal values. By treating signals as first-class citizens in query languages, we can enable seamless querying over knowledge graphs and signals. While the knowledge graph captures information on the CPS, signals represent its run-time data from sensors. We discuss the implications of such an approach and propose SigSPARQL, an extension to the SPARQL query language, to demonstrate these concepts. Furthermore, we evaluate the feasibility of implementing SigSPARQL with a prototype and demonstrate the applicability of the query language for a monitoring use case within a CPS.