# Combining Machine Learning and Ontology: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2401.07744 · HTML: https://mlsanity.com/paper/2401.07744/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 11 of its 174 references that are in the map.
- First posted on arXiv 15 Jan 2024.

## Placed in

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

## Builds on (most cited first, 11 of 11)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Ontology Reasoning with Deep Neural Networks](https://mlsanity.com/paper/1808.07980/) (2018)
- [Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases](https://mlsanity.com/paper/2102.11965/) (2021)
- [EventNet: A Large Scale Structured Concept Library for Complex Event Detection in Video](https://mlsanity.com/paper/1506.02328/) (2015)
- [Learning to Predict from Textual Data](https://mlsanity.com/paper/1402.0574/) (2014)
- [Semi-Automatic Terminology Ontology Learning Based on Topic Modeling](https://mlsanity.com/paper/1709.01991/) (2017)
- [Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology](https://mlsanity.com/paper/2101.10587/) (2021)
- [A Quality Assessment Instrument for Systematic Literature Reviews in Software Engineering](https://mlsanity.com/paper/2109.10134/) (2021)

## Built on by (most cited first, 3 of 3)

- [Detecci'on Autom'atica de Patolog'ias en Notas Cl'inicas en Espa~nol Combinando Modelos de Lenguaje y Ontolog'ias M'edicos](https://mlsanity.com/paper/2410.00616/) (2024)
- [Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems](https://mlsanity.com/paper/2506.04756/) (2025)
- [AI- and Ontology-Based Enhancements to FMEA for Advanced Systems Engineering: Current Developments and Future Directions](https://mlsanity.com/paper/2511.17743/) (2025)

## Abstract (arXiv)

Motivated by the desire to explore the process of combining inductive and deductive reasoning, we conducted a systematic literature review of articles that investigate the integration of machine learning and ontologies. The objective was to identify diverse techniques that incorporate both inductive reasoning (performed by machine learning) and deductive reasoning (performed by ontologies) into artificial intelligence systems. Our review, which included the analysis of 128 studies, allowed us to identify three main categories of hybridization between machine learning and ontologies: learning-enhanced ontologies, semantic data mining, and learning and reasoning systems. We provide a comprehensive examination of all these categories, emphasizing the various machine learning algorithms utilized in the studies. Furthermore, we compared our classification with similar recent work in the field of hybrid AI and neuro-symbolic approaches.