HGNet: Scalable Foundation Model for Automated Knowledge Graph Generation from Scientific Literature

ArXi:2603.23136v1 Announce Type: cross Automated knowledge graph (KG) construction is essential for navigating the rapidly expanding body of scientific literature. However, existing approaches struggle to recognize long multi-word entities, often fail to generalize across domains, and typically overlook the hierarchical nature of scientific knowledge. While general-purpose large language models (LLMs) offer adaptability, they are computationally expensive and yield inconsistent accuracy on specialized tasks.