Knowledge representation (KR) is vital in designing symbolic notations to
represent real-world facts and facilitate automated decision-making tasks.
Knowledge graphs (KGs) have emerged so far as a popular form of KR, offering a
contextual and human-like representation of knowledge. In international
economics, KGs have proven valuable in capturing complex interactions between
commodities, companies, and countries. By putting the gravity model, which is a
common economic framework, into the process of building KGs, important factors
that affect trade relationships can be taken into account, making it possible
to predict international trade patterns. This paper proposes an approach that
leverages Knowledge Graph embeddings for modeling international trade, focusing
on link prediction using embeddings. Thus, valuable insights are offered to
policymakers, businesses, and economists, enabling them to anticipate the
effects of changes in the international trade system. Moreover, the integration
of traditional machine learning methods with KG embeddings, such as decision
trees and graph neural networks are also explored. The research findings
demonstrate the potential for improving prediction accuracy and provide
insights into embedding explainability in knowledge representation. The paper
also presents a comprehensive analysis of the influence of embedding methods on
other intelligent algorithms.

该研究探讨了知识图谱嵌入在国际贸易中的应用及其对预测准确性和知识表示可解释性的潜力，提供了对决策者、企业家和经济学家有价值的见解。研究结果还分析了嵌入方法对其他智能算法的影响。