Large Language Models and Knowledge Graphs for Astronomical Entity Disambiguation

TL;DR

This paper explores using GPT-4 and knowledge graph clustering to extract and disambiguate astronomical entities from text, demonstrating an effective method for organizing complex astronomical information.

Contribution

It introduces a novel approach combining large language models and clustering algorithms for entity disambiguation in astronomical texts.

Findings

Effective entity disambiguation achieved

Knowledge graph clustering reveals meaningful astronomical groupings

Demonstrates potential for automated astronomical data analysis

Abstract

This paper presents an experiment conducted during a hackathon, focusing on using large language models (LLMs) and knowledge graph clustering to extract entities and relationships from astronomical text. The study demonstrates an approach to disambiguate entities that can appear in various contexts within the astronomical domain. By collecting excerpts around specific entities and leveraging the GPT-4 language model, relevant entities and relationships are extracted. The extracted information is then used to construct a knowledge graph, which is clustered using the Leiden algorithm. The resulting Leiden communities are utilized to identify the percentage of association of unknown excerpts to each community, thereby enabling disambiguation. The experiment showcases the potential of combining LLMs and knowledge graph clustering techniques for information extraction in astronomical research. The results highlight the effectiveness of the approach in identifying and disambiguating entities, as well as grouping them into meaningful clusters based on their relationships.