Authors:
Durgesh Nandini
;
Simon Blöthner
;
Mirco Schoenfeld
and
Mario Larch
Affiliation:
University of Bayreuth, Bayreuth, Germany
Keyword(s):
Knowledge Graph Embedding, Translational Embedding, KonecoTradeFlow Ontology, Multidimensional Data, International Economic Bilateral Trade Flow Data.
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.