# End-to-End Reinforcement Learning for Automatic Taxonomy Induction

arXiv: https://arxiv.org/abs/1805.04044 · HTML: https://mlsanity.com/paper/1805.04044/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2018.

## Built on by (most cited first, 17 of 17)

- [YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy](https://mlsanity.com/paper/2308.11884/) (2023)
- [Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples](https://mlsanity.com/paper/2402.07386/) (2024)
- [Taxonomy Completion via Triplet Matching Network](https://mlsanity.com/paper/2101.01896/) (2021)
- [TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic Representations](https://mlsanity.com/paper/2202.04887/) (2022)
- [Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations](https://mlsanity.com/paper/2106.02974/) (2021)
- [Prompting or Fine-tuning? A Comparative Study of Large Language Models for Taxonomy Construction](https://mlsanity.com/paper/2309.01715/) (2023)
- [Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion](https://mlsanity.com/paper/2101.11268/) (2021)
- [Distilling Hypernymy Relations from Language Models: On the Effectiveness of Zero-Shot Taxonomy Induction](https://mlsanity.com/paper/2202.04876/) (2022)
- [Who Should Go First? A Self-Supervised Concept Sorting Model for Improving Taxonomy Expansion](https://mlsanity.com/paper/2104.03682/) (2021)
- [Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision](https://mlsanity.com/paper/2203.14921/) (2022)
- [Expanding Knowledge Graphs with Humans in the Loop](https://mlsanity.com/paper/2212.05189/) (2022)
- [Probing Pretrained Language Models with Hierarchy Properties](https://mlsanity.com/paper/2312.09670/) (2023)
- [Towards Visual Taxonomy Expansion](https://mlsanity.com/paper/2309.06105/) (2023)
- [BoostTaxo: Zero-Shot Taxonomy Induction via Boosting-Style Agentic Reasoning and Constraint-Aware Calibration](https://mlsanity.com/paper/2605.12520/) (2026)
- [Building a Custom Taxonomy of AI Skills and Tasks from the Ground Up with Job Postings](https://mlsanity.com/paper/2605.21029/) (2026)
- [TaxCE : A Framework for Automated Taxonomy Construction and Evaluation at Scale](https://mlsanity.com/paper/2608.30614/) (2026)
- [Towards Automatic Evolution Tree Generation from Citation Graphs](https://mlsanity.com/paper/2609.09561/) (2026)

## Abstract (arXiv)

We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. While prior methods treat the problem as a two-phase task (i.e., detecting hypernymy pairs followed by organizing these pairs into a tree-structured hierarchy), we argue that such two-phase methods may suffer from error propagation, and cannot effectively optimize metrics that capture the holistic structure of a taxonomy. In our approach, the representations of term pairs are learned using multiple sources of information and used to determine textitwhich term to select and textitwhere to place it on the taxonomy via a policy network. All components are trained in an end-to-end manner with cumulative rewards, measured by a holistic tree metric over the training taxonomies. Experiments on two public datasets of different domains show that our approach outperforms prior state-of-the-art taxonomy induction methods up to 19.6% on ancestor F1.