| --- |
| language: |
| - en |
| - fr |
| license: cc-by-4.0 |
| task_categories: |
| - text-classification |
| - sentence-similarity |
| - feature-extraction |
| tags: |
| - ontology |
| - semantic-alignment |
| - food-science |
| - plant-biology |
| - bi-encoder |
| - fine-tuning |
| - sentence-transformers |
| - TransformON |
| - Plant-Ontology |
| - CHEBI |
| size_categories: |
| - 10K<n<100K |
| dataset_info: |
| features: |
| - name: sentence_id |
| dtype: string |
| - name: model_semantique_source |
| dtype: string |
| - name: source |
| dtype: string |
| - name: sentence |
| dtype: string |
| - name: positive_terms |
| sequence: |
| - name: term |
| dtype: string |
| - name: ontology_id |
| dtype: string |
| - name: note |
| dtype: string |
| - name: negative_terms |
| sequence: |
| - name: term |
| dtype: string |
| - name: ontology_id |
| dtype: string |
| splits: |
| - name: train |
| num_examples: 14919 |
| - name: test |
| num_examples: 100 |
| --- |
| |
| # OWS2 Dataset 1 — Semantic Alignment for Food Transformation & Plant Ontologies |
|
|
| ## Dataset Summary |
|
|
| **`ows2_dataset_1`** is a curated dataset for training and evaluating Bi-Encoder models (e.g., `all-MiniLM-L6-v2`) on **semantic ontology alignment** in the **food transformation** and plant biology domains. |
|
|
| Each example pairs a scientific sentence with a set of **positive** ontology terms (relevant concepts) and, when available, **negative** terms (hard distractors). The dataset is designed to bring complex scientific sentences and domain-specific ontology terms into a shared semantic vector space. |
|
|
| > **Note — First iteration.** This is `v1` of the dataset. The volume of real annotated data remains intentionally limited at this stage, as the dataset is expected to **grow iteratively** through successive annotation campaigns and pipeline improvements. |
|
|
| The dataset covers concepts from several ontologies: |
| - **TransformON (TFO)** — Food transformation and processing ontology |
| - **Plant Ontology (PO)** — Plant anatomy and developmental stages |
| - **Trait Ontology (TO)** — Plant traits |
|
|
| --- |
|
|
| ## Splits |
|
|
| | Split | Num. examples | Description | |
| |--------|:---:|---| |
| | `train` | 14 919 | Training split — mix of LLM-generated and real sentences | |
| | `test` | 100 | Gold standard — manually validated sentence-term pairs | |
|
|
| > **Real data volume.** As a first iteration, real annotated examples represent only a small fraction of the train split (~491 out of 14 919). The proportion of real, expert-validated data is expected to increase in future versions as annotation campaigns progress. |
|
|
| --- |
|
|
| ## Data Structure |
|
|
| Each record is a JSON object with the following fields for real data : |
|
|
| ```json |
| { |
| "sentence_id": "ABS_10.3390/antiox11010133_3", |
| "model_semantique_source": "S-PubMedBert-MS-MARCO", |
| "source": "10.3390/antiox11010133", |
| "sentence": "The canonical reactive oxygen species (ROS)-scavenging mode ...", |
| "positive_terms": [ |
| {"term": "antioxidants", "ontology_id": "TFO:tHfHTb"}, |
| {"term": "flavonoid", "ontology_id": "CHEBI:47916", "note": "Proposition manuelle sans ID d'ontologie"} |
| ], |
| "negative_terms": [ |
| {"term": "xanthophyll", "ontology_id": "TFO:2zpu01"} |
| ] |
| } |
| ``` |
|
|
| and here an example of synthetic data : |
|
|
| ```json |
| { |
| "sentence_id": "SYNTH_v0_9241", |
| "model_semantique_source": "synthetic", |
| "source": "synthetic", |
| "sentence": "The experimental group consumed a diet enriched with dehulled oak seeds, demonstrating improved digestive efficiency.", |
| "positive_terms": [ |
| {"term": "acorn, dehulled (feed)", "ontology_id": "TFO:TTN7SQ"} |
| ], |
| "negative_terms": [] |
| } |
| ``` |
|
|
| ### Fields |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `sentence_id` | `string` | Unique identifier. Prefix `ABS_` = real abstract sentence; `SYNTH_v0_` = synthetic sentence | |
| | `model_semantique_source` | `string` | Model used for candidate term retrieval (`S-PubMedBert-MS-MARCO`, `all-MiniLM-L6-v2`, `BioLORD-2023`, `synthetic`) | |
| | `source` | `string` | DOI of origin article, or `synthetic` for generated sentences | |
| | `sentence` | `string` | Scientific sentence (input text) | |
| | `positive_terms` | `list[dict]` | Relevant ontology terms (anchors for similarity) | |
| | `negative_terms` | `list[dict]` | Irrelevant terms that were rejected (hard negatives) | |
|
|
| --- |
|
|
| ## Train Split — Source Breakdown |
|
|
| | Source type | Count | |
| |---|:---:| |
| | Synthetic (LLM-generated) | 14 428 | |
| | Real scientific abstracts (DOI-based) | 491 | |
|
|
| ### A note on multiple positives and unfolding |
|
|
| Each sentence can carry **one or more positive terms** (`positive_terms` is a list). For training with pair-based losses such as MNRL, the recommended approach is to **unfold** the dataset: each `(sentence, positive_term)` pair becomes an independent training example. This increases the effective size of the training set and ensures each ontology term is explicitly associated with its sentence during optimization. |
|
|
| ```python |
| # Example unfolding |
| pairs = [ |
| (example["sentence"], term["term"]) |
| for example in dataset["train"] |
| for term in example["positive_terms"] |
| ] |
| ``` |
|
|
| ### A note on negatives |
|
|
| Synthetic examples (`SYNTH_v0_*`) — the vast majority of the train split — carry **no explicit negative terms** (`negative_terms` is an empty list). Hard negatives are only provided for manually annotated real examples. |
|
|
| To compensate for this absence during training, we rely on **in-batch negatives**: within each mini-batch, all positive terms from *other* examples act as implicit negatives for a given sentence. This strategy is made effective by using **Multiple Negatives Ranking Loss (MNRL)**, which is the recommended loss function for this dataset. The unfolding strategy described above further enriches the batch diversity, making in-batch negatives more effective. |
|
|
| --- |
|
|
| ## Test Split — Gold Standard |
|
|
| The `test` split (`dataset_goldtest.jsonl`) contains **100 manually annotated examples** drawn from real peer-reviewed scientific articles. Each sentence-term pair was validated by domain experts. |
|
|
| Articles covered include publications on: |
| - Apple polyphenols and cider chemistry |
| - Lentil texture and cooking behavior |
| - Wine oxidative stability |
| - Plant secondary metabolites and antioxidants |
|
|
| --- |
|
|
| ## Construction Pipeline |
|
|
| The dataset was built using the **DatasetBuilder** module of the OWS2 pipeline: |
|
|
| 1. **Abstract extraction** — Scientific abstracts retrieved from PubMed/ISTEX |
| 2. **Candidate term retrieval** — Top-k ontology terms retrieved via semantic similarity (Bi-Encoder) |
| 3. **Annotation** — Manual validation and positive/negative labeling via the *Check_Annotations* Streamlit app |
| 4. **Synthetic enrichment** — LLM-generated sentence-term pairs covering sparse ontology regions |
|
|
| --- |
|
|
| ## Intended Use |
|
|
| This dataset is intended for: |
| - **Fine-tuning** Bi-Encoder models (e.g., `sentence-transformers/all-MiniLM-L6-v2`) primarily with **Multiple Negatives Ranking Loss (MNRL)**, which leverages in-batch negatives to compensate for the low density of explicit hard negatives in synthetic examples |
| - **Evaluating** semantic alignment between scientific text and ontology terms (gold `test` split) |
| - **Research** on ontology-grounded sentence embeddings in **food transformation** and plant biology |
|
|
| --- |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the OWS2 project: |
|
|
| ```bibtex |
| @dataset{ows2_dataset_1, |
| title = {OWS2 Dataset 1 — Semantic Alignment for Food and Plant Ontologies}, |
| year = {2026}, |
| license = {CC BY 4.0}, |
| } |
| ``` |
| > **Affiliation:** This dataset was produced within the **OWS² (Ontology-based Workflow for Semantic Specialisation)** project, which was supported by **INRAE Metaprogramme DIGIT-BIO** within the framework of the **SEED** project, and was carried out within the **Plateforme de Profilage Métabolique et de Métabolomique (P2M2)** — UMR 1349 IGEPP, INRAE. |
| |
| --- |
|
|
| ## License |
|
|
| This dataset is released under the [Creative Commons Attribution 4.0 (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license. |
|
|
