Forecasting international trade flows is crucial for understanding global economic activity and serving as critical economic indicators used by economists and policymakers with significant implications for respective countries’ economic policies. However, this is a challenging task due to the complexity of the relationships between entities involved in international trade. While several approaches have been proposed, knowledge graph-based models have emerged as promising solutions for predicting international trade flows. In this paper, we introduce a synthetic triple-generation algorithm for enhancing downstream tasks in knowledge graph embeddings based on the graph complement. The algorithm identifies missing relationships between entities by exploiting the complement graph and generates high-quality synthetic triples that improve the accuracy of predictions. We validate the generated triples using several knowledge graphs embedding methods and computing metrics. To perform an initial result screening, an international trade scenario is explored, demonstrating the effectiveness of the proposed approach in enhancing the performance of knowledge graph-based models for predicting international trade flows.
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