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This paper explores the application of The World Avatar (TWA) dynamic knowledge graph to connect isolated data and assess the impact of rising sea levels in Singapore. Current sea level rise vulnerability assessment tools are often regional, narrow in scope (e.g., economic or cultural aspects only), and are inadequate in representing complex non-geospatial data consistently. We apply TWA to conduct a multi-perspective impact assessment of sea level rise in Singapore, evaluating vulnerable buildings, road networks, land plots, cultural sites, and populations. We introduce OntoSeaLevel, an ontology to describe sea level rise scenarios, and its impact on broader elements defined in other ontologies such as buildings (OntoBuiltEnv ontology), road networks (OpenStreetMap ontology), and land plots (Ontoplot and Ontozoning ontology). We deploy computational agents to synthesise data from government, industry, and other publicly accessible sources, enriching buildings with metadata such as property usage, estimated construction cost, number of floors, and gross floor area. An agent is applied to identify and instantiate the impacted sites using OntoSeaLevel. These sites include vulnerable buildings, land plots, cultural sites, and populations at risk. We showcase these sea level rise vulnerable elements in a unified visualisation, demonstrating TWA’s potential as a planning tool against sea level rise through vulnerability assessment, resource allocation, and integrated spatial planning.
This paper explores the application of The World Avatar (TWA) dynamic knowledge graph to connect isolated data and assess the impact of rising sea levels in Singapore. Current sea level rise vulnerability assessment tools are often regional, narrow in scope (e.g., economic or cultural aspects only), and are inadequate in representing complex non-geospatial data consistently. We apply TWA to conduct a multi-perspective impact assessment of sea level rise in Singapore, evaluating vulnerable buildings, road networks, land plots, cultural sites, and populations. We introduce OntoSeaLevel, an ontology to describe sea level rise scenarios, and its impact on broader elements defined in other ontologies such as buildings (OntoBuiltEnv ontology), road networks (OpenStreetMap ontology), and land plots (Ontoplot and Ontozoning ontology). We deploy computational agents to synthesise data from government, industry, and other publicly accessible sources, enriching buildings with metadata such as property usage, estimated construction cost, number of floors, and gross floor area. An agent is applied to identify and instantiate the impacted sites using OntoSeaLevel. These sites include vulnerable buildings, land plots, cultural sites, and populations at risk. We showcase these sea level rise vulnerable elements in a unified visualisation, demonstrating TWA’s potential as a planning tool against sea level rise through vulnerability assessment, resource allocation, and integrated spatial planning.
This study presents a framework for integrating digital twins and knowledge graphs to enhance heritage building conservation, addressing challenges in environmental stress management, material degradation, and structural integrity while preserving historical authenticity. Using validated synthetic data, the framework enables real-time monitoring, predictive maintenance, and emergency response through a digital twin connected to a knowledge graph. Four scenarios were simulated to evaluate system performance: high humidity exceeding a 75% threshold triggered alerts for limestone maintenance; temperature fluctuations caused strain levels up to 0.009 units in load-bearing components at 35 °C, necessitating structural inspection; cumulative degradation monitoring projected re-plastering needs by month eight as the plaster degradation index approached 85%; and sudden impact events simulated emergency responses, with strain spikes over 0.004 units prompting real-time alerts within 2.5 s. Response times averaged 50 ms under normal conditions, peaking at 150 ms during high-frequency updates, showing robust Application Programming Interface (API) performance and data synchronization. SPARQL (SPARQL Protocol and RDF Query Language) queries within the knowledge graph facilitated proactive maintenance scheduling, reducing reactive interventions and supporting sustainable heritage conservation, especially suited to humid–temperate climates. This framework offers a novel, structured approach that bridges modern technology with heritage preservation needs, addressing both urgent conservation challenges and long-term sustainability to ensure the resilience of heritage buildings.
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