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    一种用于军事目标发现的舰船知识图谱表示学习框架

    A Representation Learning Framework of Ship Knowledge Graph for Military Target Discovery

    • 摘要: 我国舰船编队持续扩充,在众多舰船数据中,影响较大的目标数据需要被关注。针对舰船数据难以及时分析的问题,将表示学习框架 (relational graph Transformer network, RGTN) 引入舰船知识图谱分析领域,根据舰船知识图谱的结构及语义特征,研究了一种基于表示学习的节点重要性评估方法对舰船知识图谱进行处理,实现对舰船知识图谱中节点重要性的预测。较之前舰船知识图谱的节点重要性评估算法有更好的表现,更适用于舰船知识图谱分析领域。

       

      Abstract: As China's ship fleet formation expands, the most influential data among the many ship data has become an important target that needs attention. To address the problem of difficulty in the timely analysis of ship data, are presentation learning algorithm , the Relational Graph Transformer Network (RGTN)is introduced into ship knowledge graph analysis field for the first time. Based on the structure and semantic characteristics of the ship knowledge graph, a node importance evaluation method based on representation learning is studied to process the ship knowledge graph and the importance of nodes in the ship knowledge graph is predicted. This node importance evaluation algorithm performs better than that of the previous node importance evaluation algorithm in the ship knowledge graph and is more suitable for ship knowledge graph analysis field.

       

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