Multidimensional Knowledge Graph Embeddings for International Trade Flow Analysis
This work addresses the problem of modeling high-dimensional economic data for researchers or analysts in trade analysis, but it appears incremental as it applies existing knowledge graph methods to a new domain without claiming major breakthroughs.
The paper tackles the challenge of analyzing complex, nonlinear international trade data by proposing knowledge graph embeddings, specifically implementing KonecoKG with SDM-RDFizer and AmpliGraph to predict trade relationships.
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.