Towards Semantic Smart Cities: A Study on the Conceptualization and Implementation of Semantic Context Inference...

Area of Science:

• Urban Informatics
• Artificial Intelligence
• Public Safety

Background:

• Smart cities leverage integrated urban data for services but struggle with autonomous identification of emergent threats like diseases and terrorism.
• Existing smart city frameworks lack the capability to proactively recognize and respond to unprecedented urban challenges.
• Unforeseen urban issues pose significant financial and human risks, necessitating advanced predictive capabilities.

Purpose of the Study:

• To develop a system capable of predicting potential urban issues and supporting proactive resolution.
• To enhance the autonomous identification and mitigation of emergent urban challenges within smart city environments.

Main Methods:

• Utilized semantic reasoning and the 5W1H (Who, What, When, Where, Why, How) principles as inference rules.
• Constructed a semantic graph by integrating diverse urban data sources (municipal data, IoT platforms) using domain-specific templates.
• Implemented autonomous context inference and accumulation based on 5W1H reasoning to identify situational patterns.

Main Results:

• The system successfully infers potential urban problems by detecting recurring service disruptions and their interconnections.
• Identified specific times and locations prone to urban issues through accumulated contextual data.
• Demonstrated the system's capability to connect disparate events, revealing underlying urban challenges.

Conclusions:

• Proposed a comprehensive conceptual model for a proactive urban issue prediction system.
• Provided implementation cases and use cases highlighting practical applications.
• Enhanced awareness for city administrators and citizens regarding potential problem areas, enabling preemptive mitigation strategies.