at Google ScholarTitle data
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
. - pp. 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
Project information
| Project title: |
Project's official title Project's id Berücksichtigung von kontextuellen Faktoren und strukturellen Gegebenheiten in einem dynamischen Rahmen (KONECO) 16DKWN095 |
|---|---|
| Project financing: |
Bundesministerium für Bildung, Forschung, Technologie und Raumfahrt (BMFTR) |
Abstract in another language
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.