Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications
Lu, Yuxing, Goi, Sin Yee, Zhao, Xukai, Wang, Jinzhuo
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Jan-22-2025
- Country:
- North America > United States (0.67)
- Genre:
- Overview (1.00)
- Research Report > Experimental Study (0.46)
- Industry:
- Education (1.00)
- Health & Medicine
- Therapeutic Area > Oncology (1.00)
- Pharmaceuticals & Biotechnology (1.00)
- Health Care Technology > Medical Record (1.00)
- Consumer Health (1.00)
- Diagnostic Medicine > Imaging (0.93)
- Technology:
- Information Technology
- Knowledge Management (1.00)
- Information Management (1.00)
- Data Science > Data Mining (1.00)
- Artificial Intelligence
- Natural Language > Text Processing (0.93)
- Representation & Reasoning
- Ontologies (1.00)
- Expert Systems (1.00)
- Semantic Networks (0.80)
- Rule-Based Reasoning (0.68)
- Machine Learning > Neural Networks
- Deep Learning (0.67)
- Information Technology