Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs

• URL: http://arxiv.org/abs/2307.08411v1
• Date: Mon, 17 Jul 2023 11:47:05 GMT
• Title: Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs
• Authors: Lauren Nicole DeLong, Ramon Fern\'andez Mir, Zonglin Ji, Fiona Niamh Coulter Smith, Jacques D. Fleuriot
• Abstract summary: 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.
• Score: 0.9085310904484414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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