Harnessing large language models to build knowledge graphs from earthquake news

Earthquakes are geological phenomena that have a significant impact on human lives, infrastructure, and economies. Although numerous seismic events are documented annually, only a fraction of them cause substantial damage. The media have been instrumental in collecting information on seismic events worldwide. However, existing databases often lack specific details. Consequently, scholars turn to newspaper archives to learn about aspects related to these events. Knowledge Graphs (KGs) have emerged to represent and retrieve data in a systematic way, playing a key role in artificial intelligence applications. Recent studies have explored the use of Large Language Models (LLMs) to construct KGs, but problems such as inconsistency and lack of precision persist. In this paper, a zero-shot learning approach is proposed to semi-automatically build KGs by leveraging natural language processing and semantic analysis techniques. The proposed approach aims to improve entity and relation detection while minimizing manual intervention. In addition, a set of metrics has been defined to evaluate the quality of the generated KGs, focusing on factual accuracy and semantic consistency. The effectiveness of the proposed approach is evaluated using a GDELT corpus, which contains 2,241 Spanish-language news articles on seismic events in Mexico from 2017 to 2021.