Multidimensional Knowledge Graph Embeddings for International Trade Flow Analysis

Durgesh Nandini, Simon Bloethner, Mirco Schoenfeld and Mario Larch

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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.

Date: 2024-10
New Economics Papers: this item is included in nep-knm
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