# Large Language Models and Knowledge Graphs for Astronomical Entity Disambiguation

arXiv: https://arxiv.org/abs/2406.11400 · HTML: https://mlsanity.com/paper/2406.11400/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling](https://mlsanity.com/paper/2306.11489/) (2023)
- [Enhancing Knowledge Graph Construction Using Large Language Models](https://mlsanity.com/paper/2305.04676/) (2023)
- [LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT](https://mlsanity.com/paper/2307.06917/) (2023)
- [Knowledge Graph Large Language Model (KG-LLM) for Link Prediction](https://mlsanity.com/paper/2403.07311/) (2024)
- [Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations](https://mlsanity.com/paper/2403.03008/) (2024)
- [Knowledge Graph in Astronomical Research with Large Language Models: Quantifying Driving Forces in Interdisciplinary Scientific Discovery](https://mlsanity.com/paper/2406.01391/) (2024)
- [Leveraging Large Language Models for Semantic Query Processing in a Scholarly Knowledge Graph](https://mlsanity.com/paper/2405.15374/) (2024)

## Abstract (arXiv)

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.