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Abstract：
Smart cities provide integrated management and operation of urban data emerging within a city, supplying the infrastructure for smart city services and resolving various urban challenges. Nevertheless, cities continue to grapple with substantial issues, such as contagious diseases and terrorism, that pose severe financial and human risks. These problems sporadically arise in various locales, and current smart city frameworks lack the capability to autonomously identify and address these issues. The challenge intensifies especially when trying to recognize and respond to unprecedented problems. The primary objective of this research is to predict potential urban issues and support their resolution proactively. To achieve this, our system makes use of semantic reasoning to understand the ongoing situations within the city. In this process, the 5W1H principles serve as inference rules, guiding the extraction and consolidation of context. Firstly, utilizing domain-specific annotation templates, we craft a semantic graph by amalgamating information from various sources available in the city, such as municipal public data and IoT platforms. Subsequently, the system autonomously infers and accumulates contexts of situations occurring in the city using 5W1H-based reasoning. As a result, the accumulated contexts allow for inferring potential urban problems by identifying repeated disruptions in city services at specific times or locations and establishing connections among them. The main contribution of this paper lies in proposing a comprehensive conceptual model for the suggested system and presenting actual implementation cases and applicable use cases. These contributions facilitate awareness among city administrators and citizens within a smart city regarding potential problem-prone areas or times, thereby aiding in the preemptive identification and mitigation of urban challenges.
摘要：
智慧城市提供城市内部出现的城市数据的集成管理和运营，为智慧城市服务提供基础设施，解决各种城市挑战。然而，城市继续努力解决重大问题，如传染病和恐怖主义，这构成了严重的财务和人力风险。这些问题偶尔出现在不同的地区，当前的智慧城市框架缺乏自主识别和解决这些问题的能力。挑战加剧，尤其是在试图认识和应对前所未有的问题时。这项研究的主要目的是预测潜在的城市问题并积极支持其解决。为了实现这一点，我们的系统利用语义推理来理解城市中正在发生的情况。在这个过程中,5W1H原则作为推理规则，指导上下文的提取和巩固。首先,利用特定领域的注释模板，我们通过合并来自城市中各种来源的信息来制作语义图，例如市政公共数据和物联网平台。随后，系统使用基于5W1H的推理自动推断和累积城市中发生的情况的上下文。因此,累积的环境允许通过识别特定时间或位置的城市服务中的重复中断并在它们之间建立联系来推断潜在的城市问题。本文的主要贡献在于为建议的系统提出了一个全面的概念模型，并提供了实际的实现案例和适用的用例。这些贡献促进了智慧城市中的城市管理者和市民对潜在问题多发地区或时间的认识。从而有助于先发制人地识别和缓解城市挑战。
