# Generating Categories for Sets of Entities

arXiv: https://arxiv.org/abs/2008.08428 · HTML: https://mlsanity.com/paper/2008.08428/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [InfoSync: Information Synchronization across Multilingual Semi-structured Tables](https://mlsanity.com/paper/2307.03313/) (2023)
- [Leveraging LLM For Synchronizing Information Across Multilingual Tables](https://mlsanity.com/paper/2504.02559/) (2025)
- [Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model](https://mlsanity.com/paper/2204.07014/) (2022)

## Abstract (arXiv)

Category systems are central components of knowledge bases, as they provide a hierarchical grouping of semantically related concepts and entities. They are a unique and valuable resource that is utilized in a broad range of information access tasks. To aid knowledge editors in the manual process of expanding a category system, this paper presents a method of generating categories for sets of entities. First, we employ neural abstractive summarization models to generate candidate categories. Next, the location within the hierarchy is identified for each candidate. Finally, structure-, content-, and hierarchy-based features are used to rank candidates to identify by the most promising ones (measured in terms of specificity, hierarchy, and importance). We develop a test collection based on Wikipedia categories and demonstrate the effectiveness of the proposed approach.