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Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications
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Biomedical knowledge graphs (BKGs) have emerged as powerful tools for organizing and leveraging the vast and complex data found across the biomedical field. Yet, current reviews of BKGs often limit their scope to specific domains or methods, overlooking the broader landscape and the rapid technological progress reshaping it. In this survey, we address this gap by offering a systematic review of BKGs from three core perspectives: domains, tasks, and applications. We begin by examining how BKGs are constructed from diverse data sources, including molecular interactions, pharmacological datasets, and clinical records. Next, we discuss the essential tasks enabled by BKGs, focusing on knowledge management, retrieval, reasoning, and interpretation. Finally, we highlight real-world applications in precision medicine, drug discovery, and scientific research, illustrating the translational impact of BKGs across multiple sectors. By synthesizing these perspectives into a unified framework, this survey not only clarifies the current state of BKG research but also establishes a foundation for future exploration, enabling both innovative methodological advances and practical implementations.
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Cited by 4 Pith papers
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Applying a 28-item scorecard to 16 biomedical knowledge graphs shows most lack versioning, provenance, and licensing details; only RTX-KG2 passed every check.
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Enhancing Clinical Multiple-Choice Questions Benchmarks with Knowledge Graph Guided Distractor Generation
KGGDG generates harder distractors for medical MCQs by walking a knowledge graph to find misleading paths and feeding them to an LLM, lowering LLM accuracy on most benchmarks tested.
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DoctorRAG: Medical RAG Fusing Knowledge with Patient Analogy through Textual Gradients
Combining knowledge retrieval, analogous patient case retrieval, and iterative textual-gradient refinement improves medical RAG accuracy across Chinese, English, and French benchmarks.
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