Enhancing Bilateral International Trade Flow Analysis with Knowledge Graph Embeddings

Titelangaben

Nandini, Durgesh ; Blöthner, Simon ; Schönfeld, Mirco ; Larch, Mario:
Enhancing Bilateral International Trade Flow Analysis with Knowledge Graph Embeddings.
In: Knowledge Discovery, Knowledge Engineering and Knowledge Management : 14th International Joint Conference, IC3K 2022, Valletta, Malta, October 24–26, 2022. - Cham : Springer , 2025 . - S. 177-195 . - (Communications in Computer and Information Science ; 2703 )
ISBN 978-3-032-06878-1
DOI: https://doi.org/10.1007/978-3-032-06878-1_9

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Abstract

Reliably extracting valuable information from economic data is notoriously difficult. They are the result of highly subjective actions driven by contingency and strong non-linearity, driven by high-dimensional influences. These characteristics have long posed a challenge for conventional econometric, regression-based models. To tackle this challenge, we propose using knowledge graph embeddings to analyze economic trade data, with a focus on predicting international trade relationships. We introduce KonecoKG, a knowledge graph representation of economic trade data with multidimensional relationships, built using SDM-RDFizer. We then transform these relationships into knowledge graph embeddings using AmpliGraph. Our results show that the method performs much more accurately when compared to baseline regression models.