OAMED-XMLC: a Two-Taxonomy Dataset for Benchmarking Extreme Multi-Label Classification on Medical Documents

The OAMEDXMLC dataset comprises 869'402 scientific documents, publications that are related to Surgery. It includes labeled, annotated data such as various surgery categories, domains related to the documents, authors, year of publication and references to other documents. With the help of those annotations, example tasks that can be trained using this dataset include:

Document tagging or classification among a large amount of categories (extreme multi-label classification, or XMLC)

Authors prediction

Year of publication prediction

Reference/link prediction

Note that this is an extension of the OAXMLC dataset https://zenodo.org/records/15309916

Importantly, this dataset is equipped with two independent taxonomies and set of labels, opening multiple possibilities, including

Principled investigation of the influence of taxonomies on XML algorithms

Transfer learning in XMLC (from one taxonomy to the other)

Each taxonomy is provided both in a turtle/SKOS format, as well as in a json/txt format for easier XMLC usage.

The dataset was built with data coming from the OpenAlex[OpenAlex](https://openalex.org/) open catalog. 

More detail can be found in the README.md file as well as in the original dataset https://zenodo.org/records/15309916