README.md

---
language:
- en
multilinguality:
- monolingual
dataset_info:
- config_name: pair-class
  features:
  - name: premise
    dtype: string
  - name: hypothesis
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': entailment
          '1': neutral
          '2': contradiction
- config_name: triplet-terms
  features:
  - name: anchor
    dtype: string
  - name: positive
    dtype: string
  - name: negative
    dtype: string
- config_name: triplet-sentences
  features:
  - name: anchor
    dtype: string
  - name: positive
    dtype: string
  - name: negative
    dtype: string
configs:
- config_name: pair-class
  data_files:
  - split: train
    path: pair-class/train-*
  - split: dev
    path: pair-class/dev-*
  - split: test
    path: pair-class/test-*
- config_name: triplet-terms
  data_files:
  - split: train
    path: triplet-terms/train-*
  - split: dev
    path: triplet-terms/dev-*
  - split: test
    path: triplet-terms/test-*
- config_name: triplet-sentences
  data_files:
  - split: train
    path: triplet-sentences/train-*
  - split: dev
    path: triplet-sentences/dev-*
  - split: test
    path: triplet-sentences/test-*
---

# Dataset Card for BioTermNLI

This dataset is created with a structure similar to [AllNLI](https://huggingface.co/datasets/sentence-transformers/all-nli).  It is intended for fine-tuning embedding models to generate meaningful embeddings of biomedical terms, primarily consisting of alphanumeric identifiers
such as K562, HepG2, and ZNF148.

## Raw Data

From [cellosaurus](https://www.cellosaurus.org/), we collected metadata of 30,001 cell lines that:
* Are human cell lines (NCBI_TaxID=9606)
* Have reference in [BTO](https://www.ebi.ac.uk/ols4/ontologies/bto), [CLO](https://www.ebi.ac.uk/ols4/ontologies/clo), or [EFO](https://www.ebi.ac.uk/ols4/ontologies/efo)
* With at least 1 of those annotations available:
  * Diseases: [Prostate Carcinoma](https://www.ebi.ac.uk/ols4/ontologies/ncit/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FNCIT_C4863),  [Glioblastoma](https://www.ebi.ac.uk/ols4/ontologies/ordo/classes/http%253A%252F%252Fwww.orpha.net%252FORDO%252FOrphanet_360), etc.
  * Derived from site: [lung primordium](https://www.ebi.ac.uk/ols4/ontologies/uberon/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FUBERON_0005597), [corneal epithelium](https://www.ebi.ac.uk/ols4/ontologies/uberon/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FUBERON_0001772), etc.
  * Transformant: [N-methyl-N-nitrosourea](https://www.ebi.ac.uk/chebi/chebiOntology.do?chebiId=CHEBI:50102), [Epstein Barr Virus](https://www.ebi.ac.uk/ols4/ontologies/ncbitaxon/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FNCBITaxon_10376), etc.
  * Cell type: [kidney epithelial cell](https://www.ebi.ac.uk/ols4/ontologies/cl/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FCL_0002518), [skin fibroblast](https://www.ebi.ac.uk/ols4/ontologies/cl/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FCL_0002620), etc.


## triplet-terms subset

For each annotation type, 

## pair-class subset


### Premise

each cell line and each of its available annotation (4 attributes plus category)

```python
        if annotation_type == "category":
        # Paraphrases for cell line categories (e.g., "Cancer cell line", "Hybrid cell line")
        return random.choice(
            [
                f"{cell_line_term} is a type of {annotation_term}.",
                f"{cell_line_term} cell line belongs to the category of {annotation_term}.",
                f"The {annotation_term} includes {cell_line_term}.",
            ]
        )
    elif annotation_type == "cell_type":
        # Paraphrases for cell types (e.g., "aortic endothelial cell", "retinoblast")
        return random.choice(
            [
                f"{cell_line_term} is {annotation_term}.",
                f"{annotation_term} is the cell type for {cell_line_term}.",
                f"{cell_line_term} represents a {annotation_term}.",
            ]
        )
    elif annotation_type == "disease":
        # Paraphrases for diseases (e.g., "Cutaneous Melanoma", "Chronic granulomatous disease")
        return random.choice(
            [
                f"{cell_line_term} comes from a patient with {annotation_term}.",
                f"A patient of {annotation_term} contributed to {cell_line_term}.",
                f"The donor of {cell_line_term} has {annotation_term}.",
            ]
        )
    elif annotation_type == "transformant":
        # Paraphrases for transformants (e.g., "Ad12-SV40 hybrid virus", "Human papillomavirus 38")
        return random.choice(
            [
                f"The {annotation_term} transformed {cell_line_term}.",
                f"{annotation_term} is the transformant in the {cell_line_term} cell line.",
                f"The {cell_line_term} cell line contains {annotation_term} as a transformant.",
            ]
        )
    elif annotation_type == "site":
        # Paraphrases for sites (e.g., "bone marrow", "spleen")
        return random.choice(
            [
                f"{cell_line_term} was derived from {annotation_term}.",
                f"{annotation_term} is the origin of the {cell_line_term}.",
                f"{cell_line_term} is sourced from {annotation_term}.",
            ]
        )
```

### Hypotheses


##### Entailment

Same cell line and annotation type, replaced them with synonyms if available (from cellosaurus and the ontology where the annotation comes from). Paraphrase by rule-based method.


#### Neutral

Same cell line, different annotation type


#### Contradiction

Different cell line, same annotation type

or:

Same cell line, same annotation type, wrong term?