Bridging the Climate and Environmental Data Divide: A Knowledge Graph Approach

Climate change is a pressing concern, posing significant risks to the environment and ecosystems worldwide. Severe weather events, including heatwaves, wildfires, and coastal storms, are increasingly impacting populations globally. Understanding the effects of climate change on human life is crucial for developing effective early warning systems and decision-making mechanisms to mitigate these consequences. Recent advancements in sensor technologies in environmental subsystems, such as meteorology and energy, coupled with advancements in computing and mathematical sciences, have led to substantial progress in quantifying environmental features. However, the proliferation of environmental sensor data, often fragmented and published in connection with specific projects, presents challenges for comprehensive analysis. This thesis proposes climate-centric knowledge graphs to enhance the connectivity between data from environmental subsystems. In particular, the author of this thesis addresses the problems outlined above by focusing on how environmental sensor data from various subsystems can be managed by the proposed knowledge graphs in an interoperable manner with minimum heterogeneity, how the climate can be understood better as a result of the enhanced inter-systems interoperability using the proposed knowledge graphs, and how to develop a usable data infrastructure for the proposed knowledge graph in order to meet the requirements of environmental sensor data consumers.