# ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies

arXiv: https://arxiv.org/abs/1807.05351 · HTML: https://mlsanity.com/paper/1807.05351/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2018.

## Built on by (most cited first, 15 of 15)

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- [Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research](https://mlsanity.com/paper/2403.08438/) (2024)
- [Leveraging Ontologies to Document Bias in Data](https://mlsanity.com/paper/2407.00509/) (2024)
- [A Formal Model for Artificial Intelligence Applications in Automation Systems](https://mlsanity.com/paper/2407.03183/) (2024)
- [ExeKGLib: A Platform for Machine Learning Analytics based on Knowledge Graphs](https://mlsanity.com/paper/2508.00394/) (2025)
- [Enabling Transparent Cyber Threat Intelligence Combining Large Language Models and Domain Ontologies](https://mlsanity.com/paper/2509.00081/) (2025)
- [AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment](https://mlsanity.com/paper/2602.16714/) (2026)
- [Integrating Meta-Features with Knowledge Graph Embeddings for Meta-Learning](https://mlsanity.com/paper/2603.19888/) (2026)
- [Using Large Language Models and Knowledge Graphs to Improve the Interpretability of Machine Learning Models in Manufacturing](https://mlsanity.com/paper/2604.16280/) (2026)
- [An Ontology for Machine Learning Interatomic Potentials](https://mlsanity.com/paper/2607.23219/) (2026)
- [SAREF-based Ontology for Distributed AI Workflows across the Edge-Fog-Cloud Continuum](https://mlsanity.com/paper/2608.26160/) (2026)
- [Automatically Building and Updating a Knowledge Graph of MLIP Models](https://mlsanity.com/paper/2610.09644/) (2026)

## Abstract (arXiv)

The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experiments. It can be easily extended and specialized and it is also mapped to other more domain-specific ontologies developed in the area of machine learning and data mining. In this paper we overview existing state-of-the-art machine learning interchange formats and present the first release of ML-Schema, a canonical format resulted of more than seven years of experience among different research institutions. We argue that exposing semantics of machine learning algorithms, models, and experiments through a canonical format may pave the way to better interpretability and to realistically achieve the full interoperability of experiments regardless of platform or adopted workflow solution.