Learning Hierarchical Knowledge in Text-Rich Networks with Taxonomy-Informed Representation Learning

TL;DR

This paper introduces TIER, a novel method that constructs hierarchical taxonomies within text-rich networks and integrates this structure into node representations, improving interpretability and semantic coherence.

Contribution

TIER is the first approach to explicitly incorporate hierarchical taxonomy construction into representation learning on text-rich networks, leveraging contrastive learning and clustering refinement.

Findings

TIER outperforms existing methods on multiple datasets.

Hierarchical structure improves semantic coherence.

Regularization aligns embeddings with hierarchy.

Abstract

Hierarchical knowledge structures are ubiquitous across real-world domains and play a vital role in organizing information from coarse to fine semantic levels. While such structures have been widely used in taxonomy systems, biomedical ontologies, and retrieval-augmented generation, their potential remains underexplored in the context of Text-Rich Networks (TRNs), where each node contains rich textual content and edges encode semantic relationships. Existing methods for learning on TRNs often focus on flat semantic modeling, overlooking the inherent hierarchical semantics embedded in textual documents. To this end, we propose TIER (Hierarchical \textbf{T}axonomy-\textbf{I}nformed R\textbf{E}presentation Learning on Text-\textbf{R}ich Networks), which first constructs an implicit hierarchical taxonomy and then integrates it into the learned node representations. Specifically, TIER employs similarity-guided contrastive learning to build a clustering-friendly embedding space, upon which it performs hierarchical K-Means followed by LLM-powered clustering refinement to enable semantically coherent taxonomy construction. Leveraging the resulting taxonomy, TIER introduces a cophenetic correlation coefficient-based regularization loss to align the learned embeddings with the hierarchical structure. By learning representations that respect both fine-grained and coarse-grained semantics, TIER enables more interpretable and structured modeling of real-world TRNs. We demonstrate that our approach significantly outperforms existing methods on multiple datasets across diverse domains, highlighting the importance of hierarchical knowledge learning for TRNs.