Detecting text level intellectual influence with knowledge graph embeddings

TL;DR

This paper presents a novel approach using knowledge graph embeddings and graph neural networks to predict citations between articles, revealing underlying intellectual influence and outperforming existing methods.

Contribution

It introduces a new graph neural network based embedding model for knowledge graphs that effectively predicts citation relationships between scholarly articles.

Findings

Knowledge graph embeddings outperform previous methods in citation prediction.

The model is efficient and adaptable to different corpora.

Relationships in knowledge graphs encode latent intellectual influence.

Abstract

Introduction: Tracing the spread of ideas and the presence of influence is a question of special importance across a wide range of disciplines, ranging from intellectual history to cultural analytics, computational social science, and the science of science. Method: We collect a corpus of open source journal articles, generate Knowledge Graph representations using the Gemini LLM, and attempt to predict the existence of citations between sampled pairs of articles using previously published methods and a novel Graph Neural Network based embedding model. Results: We demonstrate that our knowledge graph embedding method is superior at distinguishing pairs of articles with and without citation. Once trained, it runs efficiently and can be fine-tuned on specific corpora to suit individual researcher needs. Conclusion(s): This experiment demonstrates that the relationships encoded in a knowledge graph, especially the types of concepts brought together by specific relations can encode information capable of revealing intellectual influence. This suggests that further work in analyzing document level knowledge graphs to understand latent structures could provide valuable insights.