# Neuro-symbolic representation learning on biological knowledge graphs

arXiv: https://arxiv.org/abs/1612.04256 · HTML: https://mlsanity.com/paper/1612.04256/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2016.

## Built on by (most cited first, 25 of 25)

- [Large Language Models and Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2308.06374/) (2023)
- [Neurosymbolic AI for Reasoning over Knowledge Graphs: A Survey](https://mlsanity.com/paper/2302.07200/) (2023)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [Ontology Embedding: A Survey of Methods, Applications and Resources](https://mlsanity.com/paper/2406.10964/) (2024)
- [Neuro-Symbolic AI: Explainability, Challenges, and Future Trends](https://mlsanity.com/paper/2411.04383/) (2024)
- [Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies](https://mlsanity.com/paper/2105.04944/) (2021)
- [Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications](https://mlsanity.com/paper/2402.12608/) (2024)
- [A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives](https://mlsanity.com/paper/2503.18213/) (2025)
- [Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases](https://mlsanity.com/paper/2103.04283/) (2021)
- [Question Answering Over Biological Knowledge Graph via Amazon Alexa](https://mlsanity.com/paper/2210.06040/) (2022)
- [Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs](https://mlsanity.com/paper/2307.08411/) (2023)
- [Clinical Trial Recommendations Using Semantics-Based Inductive Inference and Knowledge Graph Embeddings](https://mlsanity.com/paper/2309.15979/) (2023)
- [From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer](https://mlsanity.com/paper/2310.08365/) (2023)
- [A Biomedical Knowledge Graph for Biomarker Discovery in Cancer](https://mlsanity.com/paper/2302.04737/) (2023)
- [Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings](https://mlsanity.com/paper/2112.04605/) (2021)
- [MARS: A neurosymbolic approach for interpretable drug discovery](https://mlsanity.com/paper/2410.05289/) (2024)
- [OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2502.18992/) (2025)
- [Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome](https://mlsanity.com/paper/2207.14294/) (2022)
- [Biomedical Knowledge Graph Embeddings with Negative Statements](https://mlsanity.com/paper/2308.03447/) (2023)
- [Navigating Healthcare Insights: A Birds Eye View of Explainability with Knowledge Graphs](https://mlsanity.com/paper/2309.16593/) (2023)
- [Integrating Symbolic Neural Networks with Building Physics: A Study and Proposal](https://mlsanity.com/paper/2411.00800/) (2024)
- [T-TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation](https://mlsanity.com/paper/2506.12075/) (2025)
- [Development of a Knowledge Graph Embeddings Model for Pain](https://mlsanity.com/paper/2308.08904/) (2023)
- [Veni, Vidi, Vici: Solving the Myriad of Challenges before Knowledge Graph Learning](https://mlsanity.com/paper/2402.06098/) (2024)
- [How Compliant is Sepsis Treatment? An Expert-Guided Neuro-symbolic Pipeline for Generating Clinical Compliance Insights](https://mlsanity.com/paper/2608.13617/) (2026)

## Abstract (arXiv)

Motivation: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics. Availability and Implementation: https://github.com/bio-ontology-research-group/walking-rdf-and-owl Contact: robert.hoehndorf@kaust.edu.sa