Datasets:

cometadata
/

affiliation-disambiguation-triplets

language:
  - en
  - multilingual
license: cc0-1.0
task_categories:
  - sentence-similarity
  - text-classification
tags:
  - affiliations
  - ror
  - triplet-loss
  - contrastive-learning
  - curriculum-learning
pretty_name: Affiliation Triplets for Embedding Training
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: anchor
      dtype: string
    - name: anchor_ror_id
      dtype: string
    - name: positive
      dtype: string
    - name: positive_ror_id
      dtype: string
    - name: negative
      dtype: string
    - name: negative_ror_id
      dtype: string
    - name: positive_similarity
      dtype: float64
    - name: negative_similarity
      dtype: float64
    - name: difficulty
      dtype: float64
    - name: negative_type
      dtype: string
    - name: triplet_id
      dtype: int64
  splits:
    - name: train
      num_bytes: 495130870
      num_examples: 1083631
  download_size: 221175576
  dataset_size: 495130870

Affiliation Triplets for Contrastive Learning

This dataset contains 1,083,631 triplets (anchor, positive, negative) for training affiliation embedding and reranking models using triplet loss or contrastive learning.

Dataset Description

Each triplet consists of:

Anchor: An affiliation string from OpenAlex
Positive: A different affiliation string for the same organization (same ROR ID)
Negative: An affiliation string for a different organization

The dataset is sorted by difficulty (descending) to support curriculum learning.

Schema

Field	Type	Description
`triplet_id`	int	Sequential ID (1-indexed), sorted by difficulty
`anchor`	string	The anchor affiliation text
`anchor_ror_id`	string	ROR ID of the anchor affiliation
`positive`	string	Positive affiliation (same org as anchor)
`positive_ror_id`	string	ROR ID of positive (same as anchor)
`negative`	string	Negative affiliation (different org)
`negative_ror_id`	string	ROR ID of negative (different from anchor)
`positive_similarity`	float	Cosine similarity between anchor and positive embeddings
`negative_similarity`	float	Cosine similarity between anchor and negative embeddings
`difficulty`	float	`positive_similarity - negative_similarity` (higher = easier)
`negative_type`	string	`"hard"` (from API candidates) or `"easy"` (random)

Statistics

Metric	Value
Total triplets	1,083,631
Hard negatives	592,300 (54.7%)
Easy negatives	491,331 (45.3%)
Difficulty range	0.00 - 1.19
Mean difficulty	0.26
Mean positive similarity	0.50
Mean negative similarity	0.24

Data Pipeline

This dataset was created through a multi-stage pipeline starting from OpenAlex affiliation data.

Ite begins by loading affiliation strings from OpenAlex that have been assigned ROR IDs, along with sample weights indicating how frequently each affiliation appears.

Next, each affiliation undergoes validation against ROR data. We verify that any country mentioned in the affiliation text matches the country in the ROR record, and that the organization's name actually appears somewhere in the affiliation string. This filtering removes assignments that are potentially incorrect.

Validated affiliations then go through a matching stage where we query the ROR affiliation matching API. We confirm that the assigned ROR ID is the same as that assigned in OpenAlex, and we collect the other candidate ROR IDs that the API returned. These candidates represent organizations that could plausibly be confused with the correct one—they become our hard negatives.

With confirmed matches in hand, we generate embeddings for all unique affiliation texts using SIRIS-Lab/affilgood-dense-retriever, producing 1024-dimensional vectors that are pre-normalized for computing cosine similarity.

We then constructs the training examples. For each anchor affiliation, we select hard negatives by finding the most similar affiliation from each candidate ROR ID, and easy negatives by randomly sampling from organizations not in the candidate set. For each negative, we find a positive (another affiliation for the same organization) that has higher similarity to the anchor than the negative does to ensure the triplet provides a valid learning signal.

Finally, we sort all triplets by difficulty in descending order and assign sequential IDs. Difficulty is computed as the gap between positive and negative similarity scores. Higher difficulty means the positive is much more similar than the negative (an easy example); lower difficulty means they're close (a hard example).

Negative Types

Hard negatives (54.7%): From ROR API candidate results - these are organizations that the API considered similar to the anchor, making them challenging negatives
Easy negatives (45.3%): Random organizations not in the API candidates - these provide baseline contrast

Related Datasets

cometadata/triplet-loss-affiliation-intermediates - Validated affiliations and confirmed matches
cometadata/triplet-loss-affiliation-embeddings - Pre-computed embeddings

Citation

If you use this dataset, please cite:

@dataset{affiliation_triplets_2025,
  title={Affiliation Triplets for Embedding Training},
  author={CoMetaData},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/cometadata/affiliation-disambiguation-triplets}
}

License

CC0-1.0