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Multidimensional Knowledge Graph Embeddings for International Trade Flow Analysis

Author

Listed:
  • Durgesh Nandini
  • Simon Bloethner
  • Mirco Schoenfeld
  • Mario Larch

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.

Suggested Citation

  • Durgesh Nandini & Simon Bloethner & Mirco Schoenfeld & Mario Larch, 2024. "Multidimensional Knowledge Graph Embeddings for International Trade Flow Analysis," Papers 2410.19835, arXiv.org.
    • Handle: RePEc:arx:papers:2410.19835
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    References listed on IDEAS

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    6. Borchert, Ingo & Larch, Mario & Shikher, Serge & Yotov, Yoto V., 2021. "The International Trade and Production Database for Estimation (ITPD-E)," International Economics, Elsevier, vol. 166(C), pages 140-166.
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