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.
@article{arxiv.2307.08411,
title = {Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs},
author = {Lauren Nicole DeLong and Ramon Fernández Mir and Zonglin Ji and Fiona Niamh Coulter Smith and Jacques D. Fleuriot},
journal= {arXiv preprint arXiv:2307.08411},
year = {2023}
}
Comments
Proceedings of the $\mathit{40}^{th}$ International Conference on Machine Learning: Workshop on Knowledge and Logical Reasoning in the Era of Data-driven Learning (https://klr-icml2023.github.io/schedule.html). PMLR 202, 2023. Condensed, workshop-ready version of previous survey, arXiv:2302.07200 , which is under review. 13 pages (9 content, 4 references), 3 figures, 1 table