OAXMLC: a Two-Taxonomy Dataset for Benchmarking Extreme Multi-Label Classification

The OAXMLC dataset comprises 3'725'870 scientific documents, publications that are related to Computer Science. It includes labeled, annotated data such as various computer science 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

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

Principled investigation of the influence of taxonomies on XML algorithms

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

The dataset was built with data coming from the OpenAlex open catalog. OpenAlex regroups entities including works, authors and institutions, as well as topics or concepts. See their official documentation for more information. Since the database of OpenAlex is in constant evolution, we downloaded a snapshot of the database on the 20th January 2025. This means that new entities added to OpenAlex after the aforementioned downloading date are not included in this dataset.

Additionally to the documents, we created two label taxonomies. These taxonomies represent categories, from the Computer Science domain, that are hierarchically organized, and on which documents are assigned to. One taxonomy is built from the OpenAlex topics and OpenAlex keywords and is further referred as the topics taxonomy. It contains 776 categories split in 3 levels. The second taxonomy is built from the OpenAlex concepts, referred as the concepts taxonomy and consists of 8'927 categories split in 5 levels. The technical details about the construction of those taxonomies are given below.

More information can be found in the README.md file.

Examples to load the dataset can be found in the OAXML_examples.ipynb file.