A multi‐source fusion and dynamic verification framework for scientific knowledge graph construction: Methods, evaluation, and applications

Abstract The exponential growth of scientific literature—over 2.5 million papers and 3.5 million patents annually—poses critical challenges for knowledge discovery. To address these, we propose SCIMKG (Scientific Knowledge Graph), a framework for constructing scientific knowledge graphs via multi‐source heterogeneous multi‐source. To address these challenges, we propose SCIMKG (Scientific Knowledge Graph), a framework for constructing scientific knowledge graphs through multi‐source heterogeneous multi‐source. SCIMKG integrates diverse academic resources including research papers, patents, experimental datasets, videos, charts, and tables to automate the construction of domain‐specific knowledge graphs. Our framework introduces three key innovations: (1) a multi‐source entity fusion mechanism employing Graph Attention Networks (GATs) with composite similarity metrics for cross‐source alignment; (2) a dynamic hybrid verification pipeline integrating semantic, statistical, and rule‐based methods; and (3) a Domain‐Adaptive Relation Extraction model (DA‐RE) utilizing adversarial training and prototype networks. Evaluations on SciBench, a multi‐domain benchmark comprising 8.2 million academic resources from computer science, biomedical engineering, and materials science, show that SCIMKG achieves 82.3% entity recognition F1‐score and 86.2% relation extraction accuracy, outperforming existing methods by 3.4 and 12.8 percentage points respectively. The constructed knowledge graph contains 14.3 million high‐confidence triples and has been deployed on a national science platform, supporting intelligent search and trend analysis. A user satisfaction survey ( N = 1,247 respondents, 95% CI [90.3%, 93.7%]) conducted across 12 institutions showed 92% satisfaction with system usability and search relevance.