mlsanityData to 14 Oct 2026

Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs

arXiv 2307.08411 ↗First posted 17 Jul 2023cs.AI cs.LG cs.LOcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 30 of its 96 references that are in the map. First posted on arXiv 17 Jul 2023.

Builds on

30 of 96 references.

References · 30

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18 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2024A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligencecited by 10
  2. 2025Defining neurosymbolic AIcited by 8
  3. 2026COTCAgent: Preventive Consultation via Probabilistic Chain-of-Thought Completioncited by 0
  4. 2026How Compliant is Sepsis Treatment? An Expert-Guided Neuro-symbolic Pipeline for Generating Clinical Compliance Insightscited by 0