DBPedia

Wikipedia- to annotate ABOUT, the curators used a Wikipedia dump and extract biography pages using named entity recognition. They labeled pages with a gender based on the number of gendered pronouns (he vs. she vs. they) and labeled each paragraph in the page with this label for the ABOUT dimension.

DBpedia (from "DB" for "database") is a project aiming to extract structured content from the information created in Wikipedia.
This is an extract of the data (after cleaning, kernel included) that provides taxonomic, hierarchical categories ("classes") for 342,782 wikipedia articles. There are 3 levels, with 9, 70 and 219 classes respectively.
A version of this dataset is a popular baseline for NLP/text classification tasks. This version of the dataset is much tougher, especially if the L2/L3 levels are used as the targets. That is why L1 and L2 will be delted and keep only L3 for the current dataset uploaded to OpenML

This is an excellent benchmark for hierarchical multiclass/multilabel text classification.
Some example approaches are included as code snippets.

Content
DBPedia dataset with multiple levels of hierarchy/classes, as a multiclass dataset.
Original DBPedia ontology (triplets data): https://wiki.dbpedia.org/develop/datasets
Listing of the class tree/taxonomy: http://mappings.dbpedia.org/server/ontology/classes/

Acknowledgements
Thanks to the Wikimedia foundation for creating Wikipedia, DBPedia and associated open-data goodness!

Thanks to my colleagues at Sparkbeyond (https://www.sparkbeyond.com) for pointing me towards the taxonomical version of this dataset (as opposed to the classic 14 class version)

paper_url = "https://arxiv.org/pdf/1509.01626"
original_data_url = "https://www.kaggle.com/datasets/danofer/dbpedia-classes"