Title data
Nandini, Durgesh ; Blöthner, Simon ; Schönfeld, Mirco ; Larch, Mario:
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. -
Porto, Portugal
,
2024
. - pp. 63-73
DOI: https://doi.org/10.5220/0013028500003838
This is the latest version of this item.
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Project information
| Project title: |
Project's official title Project's id Berücksichtigung von kontextuellen Faktoren und strukturellen Gegebenheiten in einem dynamischen Rahmen (KONECO) 16DKWN095 |
|---|---|
| Project financing: |
Bundesministerium für Bildung und Forschung |
Abstract in another language
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.
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Available Versions of this Item
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Multidimensional Knowledge Graph Embeddings for International Trade Flow Analysis. (deposited 02 Dec 2024 11:53)
- Multidimensional Knowledge Graph Embeddings for International Trade Flow Analysis. (deposited 14 Oct 2025 08:14) [Currently Displayed]

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