Blame for README.md

---
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.