用图神经网络联合提取实体并推理关系，提升复杂知识图谱性能。
Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs
- 融合图卷积与注意力机制建模图结构
- 在多个指标上优于现有深度学习模型
- 适合处理复杂知识图谱的实体与关系任务
本研究提出一种基于图神经网络的知识图谱实体抽取与关系推理算法，结合图卷积网络与图注意力网络，有效建模知识图谱的复杂结构。通过构建端到端联合模型，实现实体与关系的高效识别与推理。实验对比多种深度学习算法，结果表明该模型在AUC、召回率、精确率及F1值等指标上表现优异，尤其在复杂知识图谱中展现出更强的泛化能力与稳定性，为知识图谱后续研究提供有力支持，并验证了图神经网络在实体抽取与关系推理中的应用潜力。
原文摘要 · Abstract (English)
This study proposed a knowledge graph entity extraction and relationship reasoning algorithm based on a graph neural network, using a graph convolutional network and graph attention network to model the complex structure in the knowledge graph. By building an end-to-end joint model, this paper achieves efficient recognition and reasoning of entities and relationships. In the experiment, this paper compared the model with a variety of deep learning algorithms and verified its superiority through indicators such as AUC, recall rate, precision rate, and F1 value. The experimental results show that the model proposed in this paper performs well in all indicators, especially in complex knowledge graphs, it has stronger generalization ability and stability. This provides strong support for further research on knowledge graphs and also demonstrates the application potential of graph neural networks in entity extraction and relationship reasoning.
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