# Training Data Attribution for Image Generation using Ontology-Aligned Knowledge Graphs

arXiv: https://arxiv.org/abs/2512.02713 · HTML: https://mlsanity.com/paper/2512.02713/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 2 Dec 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Training Data Influence Analysis and Estimation: A Survey](https://mlsanity.com/paper/2212.04612/) (2022)
- [Leave-one-out Unfairness](https://mlsanity.com/paper/2107.10171/) (2021)
- [LLMs4Life: Large Language Models for Ontology Learning in Life Sciences](https://mlsanity.com/paper/2412.02035/) (2024)
- [Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting](https://mlsanity.com/paper/2507.22619/) (2025)

## Built on by (most cited first, 1 of 1)

- [FrED: External Data Influence Estimation via Domain Knowledge Graph Grounding](https://mlsanity.com/paper/2607.21615/) (2026)

## Abstract (arXiv)

As generative models become powerful, concerns around transparency, accountability, and copyright violations have intensified. Understanding how specific training data contributes to a model's output is critical. We introduce a framework for interpreting generative outputs through the automatic construction of ontologyaligned knowledge graphs (KGs). While automatic KG construction from natural text has advanced, extracting structured and ontology-consistent representations from visual content remains challenging -- due to the richness and multi-object nature of images. Leveraging multimodal large language models (LLMs), our method extracts structured triples from images, aligned with a domain-specific ontology. By comparing the KGs of generated and training images, we can trace potential influences, enabling copyright analysis, dataset transparency, and interpretable AI. We validate our method through experiments on locally trained models via unlearning, and on large-scale models through a style-specific experiment. Our framework supports the development of AI systems that foster human collaboration, creativity and stimulate curiosity.