GBN Health resilience Knowledge Graph (T3.5)

This knowledge graph dataset presents a comprehensive RDF (Resource Description Framework) representation of infectious disease mitigation strategies in Green Building Neighbourhoods (GBNs). Constructed using the Owlready2 Python library after parsing 370+ research articles through GROBID, the dataset contains over tens of thousands of individual entries semantically categorized into risks (using a PESTLE-based taxonomy), stakeholders, technologies, and interventions. The knowledge graph connects these concepts into a structured network enhanced by vector embeddings for improved search capabilities and integration with language models, creating relationships between various building and neighbourhood-scale considerations pertinent to health resilience in sustainable built environments. It incorporates both automatically extracted information and manually created blueprints that provide actionable mitigation strategies, which can be filtered and prioritized according to local contexts. This structured approach transforms disparate research findings into a navigable resource supporting evidence-based decision-making for pandemic resilience. The knowledge graph serves as a bridge between traditional literature reviews and modern LLM-powered knowledge management, enabling stakeholders to efficiently leverage collective intelligence when addressing the complex challenges of disease transmission in urban environments.