# Multidimensional Knowledge Graph Embeddings for International Trade Flow Analysis

arXiv: https://arxiv.org/abs/2410.19835 · HTML: https://mlsanity.com/paper/2410.19835/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 19 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [Graph Learning: A Survey](https://mlsanity.com/paper/2105.00696/) (2021)
- [Domain-specific Knowledge Graphs: A survey](https://mlsanity.com/paper/2011.00235/) (2020)
- [Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications](https://mlsanity.com/paper/2001.10488/) (2020)
- [Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings](https://mlsanity.com/paper/2310.11161/) (2023)

## Built on by (most cited first, 2 of 2)

- [SemCAFE: When Named Entities make the Difference Assessing Web Source Reliability through Entity-level Analytics](https://mlsanity.com/paper/2504.08776/) (2025)
- [Towards Structured Knowledge: Advancing Triple Extraction from Regional Trade Agreements using Large Language Models](https://mlsanity.com/paper/2510.05121/) (2025)

## Abstract (arXiv)

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.