Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs
This is an incremental survey paper that reviews existing methods for biomedical researchers.
The paper surveys neurosymbolic AI approaches for reasoning on biomedical knowledge graphs, highlighting their potential to improve predictions for tasks like drug repositioning by combining rule-based and embedding-based methods.
Biomedical datasets are often modeled as knowledge graphs (KGs) because they capture the multi-relational, heterogeneous, and dynamic natures of biomedical systems. KG completion (KGC), can, therefore, help researchers make predictions to inform tasks like drug repositioning. While previous approaches for KGC were either rule-based or embedding-based, hybrid approaches based on neurosymbolic artificial intelligence are becoming more popular. Many of these methods possess unique characteristics which make them even better suited toward biomedical challenges. Here, we survey such approaches with an emphasis on their utilities and prospective benefits for biomedicine.