# Enhancing Large Language Models through Neuro-Symbolic Integration and Ontological Reasoning

arXiv: https://arxiv.org/abs/2504.07640 · HTML: https://mlsanity.com/paper/2504.07640/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 7 of 7)

- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!](https://mlsanity.com/paper/2405.11706/) (2024)
- [On the Limitations of Large Language Models (LLMs): False Attribution](https://mlsanity.com/paper/2404.04631/) (2024)
- [A Short Review for Ontology Learning: Stride to Large Language Models Trend](https://mlsanity.com/paper/2404.14991/) (2024)
- [RoLD: Robot Latent Diffusion for Multi-task Policy Modeling](https://mlsanity.com/paper/2403.07312/) (2024)
- [Towards Ontology-Enhanced Representation Learning for Large Language Models](https://mlsanity.com/paper/2405.20527/) (2024)
- [Model Inversion Attacks on Homogeneous and Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2310.09800/) (2023)

## Built on by (most cited first, 4 of 4)

- [The Knowledge-Reasoning Dissociation: Fundamental Limitations of LLMs in Clinical Natural Language Inference](https://mlsanity.com/paper/2508.10777/) (2025)
- [A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification](https://mlsanity.com/paper/2605.16383/) (2026)
- [SymDiag: Explainable Diagnosis for LLM Reasoning via Neuro-Symbolic Verification](https://mlsanity.com/paper/2608.08786/) (2026)
- [Ontology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI](https://mlsanity.com/paper/2610.00682/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) demonstrate impressive capabilities in natural language processing but suffer from inaccuracies and logical inconsistencies known as hallucinations. This compromises their reliability, especially in domains requiring factual accuracy. We propose a neuro-symbolic approach integrating symbolic ontological reasoning and machine learning methods to enhance the consistency and reliability of LLM outputs. Our workflow utilizes OWL ontologies, a symbolic reasoner (e.g., HermiT) for consistency checking, and a lightweight machine learning model (logistic regression) for mapping natural language statements into logical forms compatible with the ontology. When inconsistencies between LLM outputs and the ontology are detected, the system generates explanatory feedback to guide the LLM towards a corrected, logically coherent response in an iterative refinement loop. We present a working Python prototype demonstrating this pipeline. Experimental results in a defined domain suggest significant improvements in semantic coherence and factual accuracy of LLM outputs, showcasing the potential of combining LLM fluency with the rigor of formal semantics.