Hui97/LLMOwlR

LLM4Proof Prompt Learning Dataset

This dataset contains prompt-learning samples for generating and evaluating OWL ontology proofs. It is derived from the prompt_learning_dataset.zip artifact in the LLMOwlR/LLM4Proof repository.

Each row contains a reasoning query, a shuffled list of candidate axioms, and the indices of the minimal support axioms in that shuffled list. Natural-language and OWL-formatted variants are represented as separate rows.

Atomic Distance

atomic_distance follows the metric used in the paper for selecting target conclusions. For an inferred atomic subsumption A ⊑ B, where A and B are atomic concepts, it is a heuristic estimate of reasoning length: roughly, the length of the shortest direct-subsumption chain connecting A to B, which also indicates about how many intermediate atomic concepts are needed between the two concepts. A direct subsumption has atomic distance 1; larger values usually indicate longer or more complex reasoning.

Dataset Viewer

The default configuration combines all three ontology subsets in one test split. Use the ontology column to filter for foodon, go-plus, or snomedCT.

Configuration	Split	Rows	Source file
default	test	1,969	data/*.jsonl

from datasets import load_dataset

dataset = load_dataset("Hui97/LLMOwlR", split="test")
foodon = dataset.filter(lambda row: row["ontology"] == "foodon")

Data Structure

The prepared Hugging Face layout is:

huggingface/
├── README.md
├── data/
│   ├── foodon.jsonl
│   ├── go-plus.jsonl
│   └── snomedCT.jsonl
├── metadata/
│   └── dataset_summary.json
├── prepare_dataset.py
└── upload_dataset.py

JSONL columns:

• ontology: ontology subset name, such as foodon, go-plus, or snomedCT
• atomic_distance: proof-distance bucket extracted from the original data; foodon and go-plus use 4, 6, 8, 10, 12, 14, 16, while snomedCT uses 1 and 11
• query_id: source query id
• format: natural_language or owl
• query: prompt query
• axioms: shuffled candidate support axioms
• correct_axiom_indices: indices in axioms that form the gold support set
• correct_axioms: gold support axiom text resolved from correct_axiom_indices
• source_path: path inside the original zip archive

Additional aggregate metadata is stored in metadata/dataset_summary.json. It is intentionally kept outside data/ so that the Hugging Face dataset viewer only parses the JSONL data files.

Preparation

From the repository root:

python huggingface/prepare_dataset.py \
  --input prompt_learning_dataset.zip \
  --output huggingface/data

Upload

Authenticate first:

export HF_TOKEN=<your_hugging_face_token>

Then upload:

python huggingface/upload_dataset.py --repo-id Hui97/LLMOwlR

The public dataset URL is:

https://huggingface.co/datasets/Hui97/LLMOwlR

Override the target with --repo-id or HF_REPO_ID if the dataset should live under a different Hugging Face namespace.

Citation

@inproceedings{yang2026large,
  title = {Large Language Model for OWL Proofs},
  author = {Yang, Hui and Chen, Jiaoyan and Sattler, Uli},
  booktitle = {Proceedings of the ACM Web Conference 2026},
  pages = {3952--3963},
  year = {2026},
  publisher = {ACM},
  doi = {10.1145/3774904.3792395},
  url = {https://doi.org/10.1145/3774904.3792395}
}