Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications

2025-01-20

AI Summary

Current research and applications of biomedical knowledge graphs (BKGs) lack a systematic survey, particularly regarding domain coverage, task taxonomy, and real-world deployment. Method: This paper introduces the first “Domain–Task–Application” three-dimensional analytical framework to comprehensively synthesize BKG construction methodologies across heterogeneous data sources—including molecular interactions, pharmacological databases, and clinical records—as well as core tasks (knowledge management, retrieval, reasoning, and explainability) and implementation pathways. It integrates techniques spanning knowledge extraction, graph construction, graph embedding, symbolic logical reasoning, and explainable AI, augmented with clinical NLP, drug network modeling, and multimodal alignment capabilities. Contribution/Results: The study yields a structured research landscape, clarifies technological evolution trends and engineering bottlenecks, and establishes a standardized methodology to advance precision medicine, drug discovery, and scientific discovery.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Applications
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

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.

Problem

• Biomedical Knowledge Graphs
• Application Domains
• Real-world Scenarios

Innovation

• Biomedical Knowledge Graphs
• Comprehensive Review
• Real-world Applications