# Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings

arXiv: https://arxiv.org/abs/1902.00913 · HTML: https://mlsanity.com/paper/1902.00913/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2019.

## Built on by (most cited first, 30 of 49)

- [Hyperbolic Image-Text Representations](https://mlsanity.com/paper/2304.09172/) (2023)
- [Compositional Entailment Learning for Hyperbolic Vision-Language Models](https://mlsanity.com/paper/2410.06912/) (2024)
- [Hyperbolic Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/2202.13852/) (2022)
- [ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities](https://mlsanity.com/paper/2104.02137/) (2021)
- [Hyperbolic Deep Learning for Foundation Models: A Survey](https://mlsanity.com/paper/2507.17787/) (2025)
- [Position: Beyond Euclidean -- Foundation Models Should Embrace Non-Euclidean Geometries](https://mlsanity.com/paper/2504.08896/) (2025)
- [HYPE: Hyperbolic Entailment Filtering for Underspecified Images and Texts](https://mlsanity.com/paper/2404.17507/) (2024)
- [Hyperbolic Safety-Aware Vision-Language Models](https://mlsanity.com/paper/2503.12127/) (2025)
- [HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning](https://mlsanity.com/paper/2109.10500/) (2021)
- [Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion](https://mlsanity.com/paper/2101.11268/) (2021)
- [Small Transformers Compute Universal Metric Embeddings](https://mlsanity.com/paper/2209.06788/) (2022)
- [Hyperbolic Learning with Synthetic Captions for Open-World Detection](https://mlsanity.com/paper/2404.05016/) (2024)
- [Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph Embeddings](https://mlsanity.com/paper/2103.14930/) (2021)
- [Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field](https://mlsanity.com/paper/2412.08258/) (2024)
- [Modelling Commonsense Properties using Pre-Trained Bi-Encoders](https://mlsanity.com/paper/2210.02771/) (2022)
- [Low-distortion and GPU-compatible Tree Embeddings in Hyperbolic Space](https://mlsanity.com/paper/2502.17130/) (2025)
- [HyFI: Hyperbolic Feature Interpolation for Brain-Vision Alignment](https://mlsanity.com/paper/2603.22721/) (2026)
- [Ontology Completion with Natural Language Inference and Concept Embeddings: An Analysis](https://mlsanity.com/paper/2403.17216/) (2024)
- [Path-Decoupled Hyperbolic Flow Matching for Few-Shot Adaptation](https://mlsanity.com/paper/2602.20479/) (2026)
- [Augmenting the User-Item Graph with Textual Similarity Models](https://mlsanity.com/paper/2109.09358/) (2021)
- [Hyperbolic Disentangled Representation for Fine-Grained Aspect Extraction](https://mlsanity.com/paper/2112.09215/) (2021)
- [Shadow Cones: A Generalized Framework for Partial Order Embeddings](https://mlsanity.com/paper/2305.15215/) (2023)
- [Global and Local Entailment Learning for Natural World Imagery](https://mlsanity.com/paper/2506.21476/) (2025)
- [HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings](https://mlsanity.com/paper/2204.02058/) (2022)
- [Cross-lingual Word Embeddings in Hyperbolic Space](https://mlsanity.com/paper/2205.01907/) (2022)
- [A Geometry-Aware Algorithm to Learn Hierarchical Embeddings in Hyperbolic Space](https://mlsanity.com/paper/2407.16641/) (2024)
- [Hierarchical Level-Wise News Article Clustering via Multilingual Matryoshka Embeddings](https://mlsanity.com/paper/2506.00277/) (2025)
- [Learning Protein-Ligand Binding in Hyperbolic Space](https://mlsanity.com/paper/2508.15480/) (2025)
- [HCCM: Hierarchical Cross-Granularity Contrastive and Matching Learning for Natural Language-Guided Drones](https://mlsanity.com/paper/2508.21539/) (2025)
- [Uncertainty-guided Compositional Alignment with Part-to-Whole Semantic Representativeness in Hyperbolic Vision-Language Models](https://mlsanity.com/paper/2603.22042/) (2026)

## Abstract (arXiv)

We consider the task of inferring is-a relationships from large text corpora. For this purpose, we propose a new method combining hyperbolic embeddings and Hearst patterns. This approach allows us to set appropriate constraints for inferring concept hierarchies from distributional contexts while also being able to predict missing is-a relationships and to correct wrong extractions. Moreover -- and in contrast with other methods -- the hierarchical nature of hyperbolic space allows us to learn highly efficient representations and to improve the taxonomic consistency of the inferred hierarchies. Experimentally, we show that our approach achieves state-of-the-art performance on several commonly-used benchmarks.