  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.
