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.

通过使用大型语言模型 (LLMs) 和知识图谱聚类来提取天文文本中的实体和关系，本研究在黑客马拉松期间进行了一项实验，并演示了一种消歧实体的方法，该实体可在天文领域的各种上下文中出现。通过收集特定实体周围的摘录并利用 GPT-4 语言模型，提取相关实体和关系。然后使用利登算法对这些提取的信息构建知识图谱，并对图谱进行聚类。利登聚类结果被用来识别未知摘录与每个聚类的关联度百分比，从而实现消歧。实验展示了在天文研究中将 LLMs 和知识图谱聚类技术相结合进行信息提取的潜力，结果强调了该方法在识别和消歧实体以及基于关系对它们进行有意义聚类方面的有效性。