The accumulation of micro- and nano-plastics in various environmental media raises significant health concerns, including effects on respiratory, gastrointestinal, and neurological systems. This study introduces a framework that employs large language models to delineate complex relationships between pollutants and health outcomes, providing a structured approach to understanding these interactions.
By identifying and constructing multi-hop semantic chains from scientific texts, the system aggregates relational metapaths into a Toxicity Trajectory Graph. This graph visualizes the pathways through which pollutants affect biological systems, enabling researchers to pinpoint critical exposure routes. Additionally, a dynamic evidence reconciliation module enhances the framework by addressing semantic conflicts that arise from divergent research findings, ensuring a more reliable extraction of information.
The implications of this approach are substantial, as it allows for scalable, accurate mining of intricate cause-effect relationships within scientific literature. However, the reliance on evolving datasets and the need for consistent updates pose risks to its applicability. Overall, this innovative methodology not only aids in risk assessment but also enhances our understanding of the health impacts associated with plastic pollution.
👉 Pročitaj original: arXiv AI Papers
