Knowledge graphs have gained popularity for their ability to organize and analyze complex data effectively. When combined with graph embedding techniques, such as graph neural networks (GNNs), knowledge graphs become a potent tool in providing valuable insights. This study explores the application of graph embedding in identifying competitors from a financial knowledge graph. Existing state-of-the-art(SOTA) models face challenges due to the unique attributes of our knowledge graph, including directed and undirected relationships, attributed nodes, and minimal annotated competitor connections. To address these challenges, we propose a novel graph embedding model, JPEC(JPMorgan Proximity Embedding for Competitor Detection), which utilizes graph neural network to learn from both first-order and second-order node proximity together with vital features for competitor retrieval. JPEC had outperformed most existing models in extensive experiments, showcasing its effectiveness in competitor retrieval.

翻译：知识图谱因其有效组织和分析复杂数据的能力而受到广泛关注。当与图嵌入技术（如图神经网络）结合时，知识图谱成为提供宝贵洞察的有力工具。本研究探讨了图嵌入在从金融知识图谱中识别竞争对手的应用。现有最先进模型因我们知识图谱的独特属性而面临挑战，这些属性包括有向与无向关系、带属性节点以及极少的标注竞争对手连接。为应对这些挑战，我们提出了一种新颖的图嵌入模型JPEC（JPMorgan Proximity Embedding for Competitor Detection），该模型利用图神经网络，结合一阶与二阶节点邻近性以及关键特征进行竞争对手检索。在大量实验中，JPEC的表现超越了大多数现有模型，证明了其在竞争对手检索中的有效性。