Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823003439 |
| _version_ | 1797389573790105600 |
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| author | Diego Rincon-Yanez Chahinez Ounoughi Bassem Sellami Tarmo Kalvet Marek Tiits Sabrina Senatore Sadok Ben Yahia |
| author_facet | Diego Rincon-Yanez Chahinez Ounoughi Bassem Sellami Tarmo Kalvet Marek Tiits Sabrina Senatore Sadok Ben Yahia |
| author_sort | Diego Rincon-Yanez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T22:57:53Z |
| format | Article |
| id | doaj.art-c4eb64dab0fc4c8d912cf0b7bac82ae7 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-03-08T22:57:53Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-c4eb64dab0fc4c8d912cf0b7bac82ae72023-12-16T06:06:00ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-12-013510101789Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddingsDiego Rincon-Yanez0Chahinez Ounoughi1Bassem Sellami2Tarmo Kalvet3Marek Tiits4Sabrina Senatore5Sadok Ben Yahia6Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Via Giovanni Paolo II 132, Fisciano, 84084, Italy; Corresponding author.Department of Software Science, Tallinn University of Technology, Akadeemia tee 15a, Tallinn, 12618, EstoniaDepartment of Software Science, Tallinn University of Technology, Akadeemia tee 15a, Tallinn, 12618, Estonia; Faculty of Sciences of Tunis, University of Tunis El Manar, Campus universitaire, Tunis, 1092, TunisiaDepartment of Business Administration, Tallinn University of Technology, Ehitajate tee 5, Tallinn, 12618, EstoniaDepartment of Business Administration, Tallinn University of Technology, Ehitajate tee 5, Tallinn, 12618, EstoniaDepartment of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Via Giovanni Paolo II 132, Fisciano, 84084, ItalyDepartment of Software Science, Tallinn University of Technology, Akadeemia tee 15a, Tallinn, 12618, EstoniaKnowledge 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.http://www.sciencedirect.com/science/article/pii/S1319157823003439Knowledge GraphKnowledge Graph EmbeddingsGravity modelInternational Trade |
| spellingShingle | Diego Rincon-Yanez Chahinez Ounoughi Bassem Sellami Tarmo Kalvet Marek Tiits Sabrina Senatore Sadok Ben Yahia Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings Journal of King Saud University: Computer and Information Sciences Knowledge Graph Knowledge Graph Embeddings Gravity model International Trade |
| title | Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings |
| title_full | Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings |
| title_fullStr | Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings |
| title_full_unstemmed | Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings |
| title_short | Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings |
| title_sort | accurate prediction of international trade flows leveraging knowledge graphs and their embeddings |
| topic | Knowledge Graph Knowledge Graph Embeddings Gravity model International Trade |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823003439 |
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