Improving Biomedical Knowledge Graph Construction through Large Language Model Driven Literature Mining and Semantic Relationship Extraction - Jeremy Langston; Patrick Pennington; Gordon Pennington; Tristan Whitaker (Bioinformatics Insights and Analytics, 2026)
Biomedical knowledge graphs have emerged as foundational infrastructures for organizing heterogeneous biomedical information across genomics, clinical medicine, pharmacology, public health, and translational research ecosystems. However, the rapid expansion of scientific literature, fragmented biomedical ontologies, inconsistent terminologies, and continuously evolving semantic relationships have significantly constrained the scalability and reliability of conventional biomedical knowledge graph construction methodologies. Traditional rule-based extraction pipelines and manually curated semantic integration frameworks frequently struggle to maintain semantic consistency, contextual precision, and adaptive scalability under contemporary data growth conditions. Recent advances in large language models have introduced transformative possibilities for literature mining, semantic reasoning, entity alignment, and contextual relationship extraction across large-scale biomedical corpora. This paper examines the architectural transformation of biomedical knowledge graph construction through large language model driven literature mining and semantic relationship extraction systems. The study analyzes the integration of transformer-based language architectures into biomedical information pipelines, focusing on system interoperability, semantic robustness, governance infrastructures, computational sustainability, and deployment challenges across distributed biomedical environments. Particular attention is devoted to the interaction between biomedical ontologies, language model reasoning capabilities, domain adaptation mechanisms, and human-in-the-loop validation systems. The paper further evaluates structural trade-offs between automation and interpretability, centralized and federated infrastructures, and generative inference and symbolic biomedical reasoning. Through a systems-oriented analysis, the study demonstrates that large language model enhanced biomedical knowledge graph ecosystems can significantly improve semantic coverage, contextual accuracy, and cross-domain integration while simultaneously introducing new governance, bias, reproducibility, and infrastructural risks. The paper concludes by proposing a future-oriented framework for sustainable, trustworthy, and policy-aware biomedical knowledge graph infrastructures capable of supporting next-generation precision medicine and translational biomedical discovery.