Multidimensional Knowledge Graph Embeddings for International Trade Flow Analysis

Durgesh Nandini, Simon Blöthner, Mirco Schoenfeld, Mario Larch

2024

Abstract

Understanding the complex dynamics of high-dimensional, contingent, and strongly nonlinear economic data, often shaped by multiplicative processes, poses significant challenges for traditional regression methods as such methods offer limited capacity to capture the structural changes they feature. To address this, we propose leveraging the potential of knowledge graph embeddings for economic trade data, in particular, to predict international trade relationships. We implement KonecoKG, a knowledge graph representation of economic trade data with multidimensional relationships using SDM-RDFizer and transform the relationships into a knowledge graph embedding using AmpliGraph.

Paper Citation

in Harvard Style

Nandini D., Blöthner S., Schoenfeld M. and Larch M. (2024). Multidimensional Knowledge Graph Embeddings for International Trade Flow Analysis. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD; ISBN 978-989-758-716-0, SciTePress, pages 63-73. DOI: 10.5220/0013028500003838

in Bibtex Style

@conference{keod24,
author={Durgesh Nandini and Simon Blöthner and Mirco Schoenfeld and Mario Larch},
title={Multidimensional Knowledge Graph Embeddings for International Trade Flow Analysis},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD},
year={2024},
pages={63-73},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013028500003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD
TI - Multidimensional Knowledge Graph Embeddings for International Trade Flow Analysis
SN - 978-989-758-716-0
AU - Nandini D.
AU - Blöthner S.
AU - Schoenfeld M.
AU - Larch M.
PY - 2024
SP - 63
EP - 73
DO - 10.5220/0013028500003838
PB - SciTePress