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Knowledge representation (KR) is vital in designing symbolic notations torepresent real-world facts and facilitate automated decision-making tasks.Knowledge graphs (KGs) have emerged so far as a popular form of KR, offering acontextual and human-like representation of knowledge. In internationaleconomics, KGs have proven valuable in capturing complex interactions betweencommodities, companies, and countries. By putting the gravity model, which is acommon economic framework, into the process of building KGs, important factorsthat affect trade relationships can be taken into account, making it possibleto predict international trade patterns. This paper proposes an approach thatleverages Knowledge Graph embeddings for modeling international trade, focusingon link prediction using embeddings. Thus, valuable insights are offered topolicymakers, businesses, and economists, enabling them to anticipate theeffects of changes in the international trade system. Moreover, the integrationof traditional machine learning methods with KG embeddings, such as decisiontrees and graph neural networks are also explored. The research findingsdemonstrate the potential for improving prediction accuracy and provideinsights into embedding explainability in knowledge representation. The paperalso presents a comprehensive analysis of the influence of embedding methods onother intelligent algorithms.

Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings