# Benchmarking Recommendation, Classification, and Tracing Based on Hugging Face Knowledge Graph

arXiv: https://arxiv.org/abs/2505.17507 · HTML: https://mlsanity.com/paper/2505.17507/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2025.

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

- [HugSelect: An Explainable Multi-Criteria Decision-Support Framework for foundation-model selection](https://mlsanity.com/paper/2608.08069/) (2026)
- [Integrating Meta-Features with Knowledge Graph Embeddings for Meta-Learning](https://mlsanity.com/paper/2603.19888/) (2026)
- [ArtifactLinker: Linking Scientific Artifacts for Automatic State-of-the-Art Discovery](https://mlsanity.com/paper/2605.16902/) (2026)

## Abstract (arXiv)

The rapid growth of open source machine learning (ML) resources, such as models and datasets, has accelerated IR research. However, existing platforms like Hugging Face do not explicitly utilize structured representations, limiting advanced queries and analyses such as tracing model evolution and recommending relevant datasets. To fill the gap, we construct HuggingKG, the first large-scale knowledge graph built from the Hugging Face community for ML resource management. With 2.6 million nodes and 6.2 million edges, HuggingKG captures domain-specific relations and rich textual attributes. It enables us to further present HuggingBench, a multi-task benchmark with three novel test collections for IR tasks including resource recommendation, classification, and tracing. Our experiments reveal unique characteristics of HuggingKG and the derived tasks. Both resources are publicly available, expected to advance research in open source resource sharing and management.