| --- |
| 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` | |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("Hui97/LLMOwlR", split="test") |
| foodon = dataset.filter(lambda row: row["ontology"] == "foodon") |
| ``` |
|
|
| ## Data Structure |
|
|
| Repository files: |
|
|
| ```text |
| 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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
