Towards Structured Knowledge: Advancing Triple Extraction from Regional Trade Agreements Using Large Language Models

Title data

Nandini, Durgesh ; Koch, Rebekka ; Schönfeld, Mirco:
Towards Structured Knowledge: Advancing Triple Extraction from Regional Trade Agreements Using Large Language Models.
In: Web Engineering : 25th International Conference, ICWE 2025, Delft, The Netherlands, June 30 – July 3, 2025 ; Proceedings. - Cham : Springer , 2025 . - pp. 3-10 . - (Lecture Notes in Computer Science ; 15749 )
ISBN 978-3-031-97207-2
DOI: https://doi.org/10.1007/978-3-031-97207-2_1

Project information

Abstract in another language

This study investigates the effectiveness of Large Language Models (LLMs) for the extraction of structured knowledge in the form of Subject-Predicate-Object triples. We apply the setup for the domain of Economics application. The findings can be applied to a wide range of scenarios, including the creation of economic trade knowledge graphs from natural language legal trade agreement texts. As a use case, we apply the model to regional trade agreement texts to extract trade-related information triples. In particular, we explore the zero-shot, one-shot and few-shot prompting techniques, incorporating positive and negative examples, and evaluate their performance based on quantitative and qualitative metrics. Specifically, we used the Llama 3.1 model to process the unstructured regional trade agreement texts and extract triples. We discuss key insights, challenges, and potential future directions, emphasizing the significance of language models in economic applications.