Datasets:

Hui97
/

LLMOwlR

pretty_name: LLM4Proof Prompt Learning Dataset
language:
  - en
license: other
task_categories:
  - text-generation
  - question-answering
tags:
  - ontology
  - owl
  - description-logic
  - reasoning
  - proof-generation
  - llm4proof
configs:
  - config_name: default
    data_files:
      - split: test
        path:
          - data/foodon.jsonl
          - data/go-plus.jsonl
          - data/snomedCT.jsonl

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

Repository files:

README.md
data/
├── foodon.jsonl
├── go-plus.jsonl
└── snomedCT.jsonl
metadata/
└── dataset_summary.json

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.

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}
}