用知识图谱嵌入分析国际贸易关系，捕捉复杂非线性结构。
Multidimensional Knowledge Graph Embeddings for International Trade Flow Analysis
- 构建多维贸易关系知识图谱，融合多重经济属性
- 基于嵌入模型预测跨国贸易流向，提升结构捕捉能力
- 适合研究国际经济网络与复杂系统建模的学者
理解高维、条件依赖且强非线性的经济数据（常由乘积过程驱动）的动态特性，对传统回归方法构成重大挑战，因其难以捕捉其中的结构性变化。为应对这一问题，我们提出利用知识图谱嵌入技术处理经济贸易数据，特别是用于预测国际间贸易关系。通过SDM-RDFizer构建名为KonecoKG的经济贸易知识图谱，实现多维度关系建模，并使用AmpliGraph将关系转化为知识图谱嵌入表示，从而更有效地揭示贸易网络中的隐含模式。
原文摘要 · Abstract (English)
Understanding the complex dynamics of high-dimensional, contingent, and strongly nonlinear economic data, often shaped by multiplicative processes, poses significant challenges for traditional regression methods as such methods offer limited capacity to capture the structural changes they feature. To address this, we propose leveraging the potential of knowledge graph embeddings for economic trade data, in particular, to predict international trade relationships. We implement KonecoKG, a knowledge graph representation of economic trade data with multidimensional relationships using SDM-RDFizer, and transform the relationships into a knowledge graph embedding using AmpliGraph.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。