Detecting text level intellectual influence with knowledge graph embeddings

• Main Authors: Li, Lucian, Silva, Eryclis
• Format: Journal Article
• Language: English
• Published: 31-10-2024
• Subjects: Computer Science - Computation and Language
• DOI: 10.48550/arxiv.2410.24021

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.