# Path Based Hierarchical Clustering on Knowledge Graphs

arXiv: https://arxiv.org/abs/2109.13178 · HTML: https://mlsanity.com/paper/2109.13178/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [Large-scale Taxonomy Induction Using Entity and Word Embeddings](https://mlsanity.com/paper/2105.01305/) (2021)
- [Unsupervised Hierarchical Grouping of Knowledge Graph Entities](https://mlsanity.com/paper/1908.07281/) (2019)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Blockmodelling for Knowledge Graphs](https://mlsanity.com/paper/2408.15649/) (2024)

## Abstract (arXiv)

Knowledge graphs have emerged as a widely adopted medium for storing relational data, making methods for automatically reasoning with them highly desirable. In this paper, we present a novel approach for inducing a hierarchy of subject clusters, building upon our earlier work done in taxonomy induction. Our method first constructs a tag hierarchy before assigning subjects to clusters on this hierarchy. We quantitatively demonstrate our method's ability to induce a coherent cluster hierarchy on three real-world datasets.