Taxonomy Tree Generation from Citation Graph

TL;DR

This paper introduces HiGTL, an automated framework for generating hierarchical scientific taxonomies from citation graphs, combining clustering, language models, and joint optimization to produce coherent, meaningful structures.

Contribution

The paper presents a novel end-to-end method that integrates citation graph clustering with language model-based taxonomy verbalization, guided by human instructions.

Findings

HiGTL outperforms existing methods in producing coherent taxonomies.

The framework effectively combines textual and citation data for clustering.

Joint optimization improves the semantic quality of generated taxonomies.

Abstract

Constructing taxonomies from citation graphs is essential for organizing scientific knowledge, facilitating literature reviews, and identifying emerging research trends. However, manual taxonomy construction is labor-intensive, time-consuming, and prone to human biases, often overlooking pivotal but less-cited papers. In this paper, to enable automatic hierarchical taxonomy generation from citation graphs, we propose HiGTL (Hierarchical Graph Taxonomy Learning), a novel end-to-end framework guided by human-provided instructions or preferred topics. Specifically, we propose a hierarchical citation graph clustering method that recursively groups related papers based on both textual content and citation structure, ensuring semantically meaningful and structurally coherent clusters. Additionally, we develop a novel taxonomy node verbalization strategy that iteratively generates central concepts for each cluster, leveraging a pre-trained large language model (LLM) to maintain semantic consistency across hierarchical levels. To further enhance performance, we design a joint optimization framework that fine-tunes both the clustering and concept generation modules, aligning structural accuracy with the quality of generated taxonomies. Extensive experiments demonstrate that HiGTL effectively produces coherent, high-quality taxonomies.