# CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring

arXiv: https://arxiv.org/abs/2010.06714 · HTML: https://mlsanity.com/paper/2010.06714/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 19 of 19)

- [Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations](https://mlsanity.com/paper/2202.04582/) (2022)
- [TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters](https://mlsanity.com/paper/2201.06771/) (2022)
- [Self-Supervised Euphemism Detection and Identification for Content Moderation](https://mlsanity.com/paper/2103.16808/) (2021)
- [TaxoAdapt: Aligning LLM-Based Multidimensional Taxonomy Construction to Evolving Research Corpora](https://mlsanity.com/paper/2506.10737/) (2025)
- [TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic Representations](https://mlsanity.com/paper/2202.04887/) (2022)
- [Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy](https://mlsanity.com/paper/2403.04160/) (2024)
- [Open Relation and Event Type Discovery with Type Abstraction](https://mlsanity.com/paper/2212.00178/) (2022)
- [A Survey on Knowledge Organization Systems of Research Fields: Resources and Challenges](https://mlsanity.com/paper/2409.04432/) (2024)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation](https://mlsanity.com/paper/2211.01981/) (2022)
- [generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptation](https://mlsanity.com/paper/2403.07627/) (2024)
- [LLMTaxo: Leveraging Large Language Models for Constructing Taxonomy of Factual Claims from Social Media](https://mlsanity.com/paper/2504.12325/) (2025)
- [RaTE: a Reproducible automatic Taxonomy Evaluation by Filling the Gap](https://mlsanity.com/paper/2307.09706/) (2023)
- [GitRanking: A Ranking of GitHub Topics for Software Classification using Active Sampling](https://mlsanity.com/paper/2205.09379/) (2022)
- [Building Data-Driven Occupation Taxonomies: A Bottom-Up Multi-Stage Approach via Semantic Clustering and Multi-Agent Collaboration](https://mlsanity.com/paper/2509.15786/) (2025)
- [Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model](https://mlsanity.com/paper/2406.17739/) (2024)
- [TaxoBell: Gaussian Box Embeddings for Self-Supervised Taxonomy Expansion](https://mlsanity.com/paper/2601.09633/) (2026)
- [Term-Centric Hierarchy Induction from Heterogeneous Corpora](https://mlsanity.com/paper/2606.26963/) (2026)
- [TaxCE : A Framework for Automated Taxonomy Construction and Evaluation at Scale](https://mlsanity.com/paper/2608.30614/) (2026)

## Abstract (arXiv)

Taxonomy is not only a fundamental form of knowledge representation, but also crucial to vast knowledge-rich applications, such as question answering and web search. Most existing taxonomy construction methods extract hypernym-hyponym entity pairs to organize a "universal" taxonomy. However, these generic taxonomies cannot satisfy user's specific interest in certain areas and relations. Moreover, the nature of instance taxonomy treats each node as a single word, which has low semantic coverage. In this paper, we propose a method for seed-guided topical taxonomy construction, which takes a corpus and a seed taxonomy described by concept names as input, and constructs a more complete taxonomy based on user's interest, wherein each node is represented by a cluster of coherent terms. Our framework, CoRel, has two modules to fulfill this goal. A relation transferring module learns and transfers the user's interested relation along multiple paths to expand the seed taxonomy structure in width and depth. A concept learning module enriches the semantics of each concept node by jointly embedding the taxonomy and text. Comprehensive experiments conducted on real-world datasets show that Corel generates high-quality topical taxonomies and outperforms all the baselines significantly.