The smart home brings together devices, the cloud, data, and people to make home living more comfortable and safer. Trigger–action programming enables users to connect smart devices using if-this-then-that (IFTTT)-style rules. With the increasing number of devices in smart home systems, multiple running rules that act on actuators in contradictory ways may cause unexpected and unpredictable interference problems, which can put residents and their belongings at risk. Previous studies have considered explicit interference problems related to multiple rules targeting a single actuator, whereas implicit interference (interference across different actuators) detection is still challenging and not yet well studied owing to the effort-intensive and time-consuming annotation work of obtaining device information. The lack of knowledge about devices is a critical reason that affects the accuracy and efficiency in implicit interference detection. In this article, we propose A3ID, an automatic detection method for implicit interference based on knowledge graphs. Using natural language processing (NLP) techniques and a lexical database, A3ID can extract knowledge of devices from a knowledge graph, including functionality, effect, and scope. Then, it analyzes and detects interferences among the different devices semantically in three steps, without human intervention. Furthermore, it provides user-friendly explanations in a well-designed structure to specify possible reasons for the implicit interference problems. Our experiment on 11 859 IFTTT-style rules shows that A3ID outperforms state-of-the-art methods by more than 33% in the F1-score for the detection of implicit interference. Moreover, evaluations on an extended data set for devices from ConceptNet (a knowledge graph) and five smart home systems suggest that A3ID also has favorable performance with other devices not limited to the smart home domain.

中文翻译：

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A3ID：通过知识图自动和可解释的智能家居隐式干扰检测方法

智能家居将设备，云，数据和人员聚集在一起，使家庭生活更舒适，更安全。触发操作编程使用户可以使用“先有后先”（IFTTT）样式的规则连接智能设备。随着智能家居系统中设备数量的增加，以相互矛盾的方式作用于执行器的多个运行规则可能会导致意外和不可预测的干扰问题，从而使居民及其财产受到威胁。先前的研究已经考虑了与针对单个执行器的多个规则相关的显式干扰问题，而隐式干扰（跨不同执行器的干扰）检测仍然具有挑战性，并且由于获取设备信息的工作量大且费时的注释工作而尚未得到很好的研究。 。缺少有关设备的知识是影响隐式干扰检测的准确性和效率的关键原因。在本文中，我们提出了一种A3ID，一种基于知识图的隐式干扰自动检测方法。使用自然语言处理（NLP）技术和词汇数据库，A3ID可以从知识图中提取设备的知识，包括功能，效果和范围。然后，它通过三个步骤从语义上分析和检测不同设备之间的干扰，而无需人工干预。此外，它以精心设计的结构提供了用户友好的解释，以指定隐式干扰问题的可能原因。我们对11859个IFTTT样式的规则进行的实验表明，在检测隐式干扰方面，A3ID在F1评分方面比最新方法高出33％以上。此外，对来自ConceptNet（知识图）和五个智能家居系统的设备的扩展数据集的评估表明，A3ID在不限于智能家居领域的其他设备上也具有良好的性能。