Knowledge Graphs in Environmental Science: A Critical Perspective

Knowledge Graphs (KGs) have emerged as powerful tools in environmental science, offering structured frameworks for integrating diverse datasets across ecological, climatic, and socio-environmental domains.While their utility in data organization and retrieval is well-recognized, this article critically examines the inherent limitations of KGs in capturing the dynamic complexities of environmental systems.Specifically, it explores how KGs may inadequately represent nonlinear interactions, such as feedback loops and tipping points, which are pivotal in ecological processes.Furthermore, the article highlights the epistemological and ethical concerns arising from biases embedded in source datasets, including geographic and taxonomic disparities, and the underrepresentation of Indigenous and local knowledge systems.The rigidity of predefined ontologies within KGs is also scrutinized, questioning their capacity to accommodate the fluid and evolving nature of environmental knowledge.Through this critical lens, the article advocates for a more nuanced application of KGs, emphasizing the need for integrating dynamic modeling approaches and inclusive data practices to more accurately reflect the intricacies of environmental phenomena.