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

As a result, KR is critical to offering a simple strategy for defining relevant and contextual information within a finite number of facts from a specific domain of interest; these facts are referred to as a knowledge base (KB). In the past years, Knowledge Graph (KG), as a form of KR, has gained attention because it provides a contextual, natural, and human-like form of representing knowledge in specific domains and common sense. KG is formed in statements called triples on the T = (h, r, t) form, where h (head) and t (tail) represent objects in real life, and r, the relation is the connection between those entities. Internet companies like Google, Wikipedia, and Facebook have found a simple but powerful unified tool in the KG field to describe their multi-structured and multi-dimensional knowledge base, capturing user data to transform it into vast KBs [3]. The KG approach is particularly relevant to studying international trade, a significant cornerstone of economic and social development in the globalized economy [4, 5]. International trade is complex and interconnected, with multiple entities (commodities, companies, and countries) interacting in multiple ways [6]. This method helps to understand those complex interactions in a structured and intuitive way. In international economics, the gravity model, a fundamental part of the current method, is widely used to predict trade relations between entities based on factors like size (GDP, population) and distance or other factors [7, 8, 9].