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.

翻译：知识表示（KR）是设计符号化表示以描述现实世界事实并辅助自动化决策的关键技术。知识图谱（KG）作为知识表示的重要形式，能够提供语境化、类人化的知识表征。在国际经济学领域，知识图谱已被证实可有效捕捉商品、企业与国家间的复杂交互关系。通过在知识图谱构建过程中引入作为常用经济框架的引力模型，可纳入影响贸易关系的核心要素，从而实现国际贸易模式的预测。本文提出一种基于知识图谱嵌入的国际贸易建模方法，重点利用嵌入进行链路预测。该方法可为政策制定者、企业及经济学家提供有价值洞见，助力预判国际贸易体系变动的影响。此外，本文还探讨了随机森林、图神经网络等传统机器学习方法与知识图谱嵌入的融合策略。研究结果表明，该方法能有效提升预测准确度，并揭示了嵌入机制在知识表示中的可解释性。最后，本文系统分析了嵌入方法对其他智能算法的影响规律。