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[2509.00287] SIGMUS: Semantic Integration for Knowledge Graphs in Multimodal Urban Spaces
Summary
The paper presents SIGMUS, a system for semantic integration of multimodal data in urban environments, leveraging large language models to enhance incident understanding and forecasting.
Why It Matters
As urban areas increasingly rely on diverse sensor data, integrating this information is crucial for effective incident management. SIGMUS addresses the challenge of fragmented data sources, improving the ability to analyze and respond to urban incidents, which is vital for public safety and urban planning.
Key Takeaways
- SIGMUS integrates multimodal data sources for better incident analysis.
- Utilizes large language models to identify relationships in urban data.
- Enhances forecasting capabilities for urban incidents.
- Organizes knowledge into a structured graph for improved accessibility.
- Addresses the limitations of human-driven reasoning in data integration.
Computer Science > Artificial Intelligence arXiv:2509.00287 (cs) [Submitted on 30 Aug 2025 (v1), last revised 17 Feb 2026 (this version, v2)] Title:SIGMUS: Semantic Integration for Knowledge Graphs in Multimodal Urban Spaces Authors:Brian Wang, Mani Srivastava View a PDF of the paper titled SIGMUS: Semantic Integration for Knowledge Graphs in Multimodal Urban Spaces, by Brian Wang and 1 other authors View PDF HTML (experimental) Abstract:Modern urban spaces are equipped with an increasingly diverse set of sensors, all producing an abundance of multimodal data. Such multimodal data can be used to identify and reason about important incidents occurring in urban landscapes, such as major emergencies, cultural and social events, as well as natural disasters. However, such data may be fragmented over several sources and difficult to integrate due to the reliance on human-driven reasoning for identifying relationships between the multimodal data corresponding to an incident, as well as understanding the different components which define an incident. Such relationships and components are critical to identifying the causes of such incidents, as well as producing forecasting the scale and intensity of future incidents as they begin to develop. In this work, we create SIGMUS, a system for Semantic Integration for Knowledge Graphs in Multimodal Urban Spaces. SIGMUS uses Large Language Models (LLMs) to produce the necessary world knowledge for identifying relationships between inciden...
