--- 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 **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.