[论文解读] Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs

Junliang Du, Guiran Liu+4 more|arXiv (Cornell University)|Nov 19, 2024

Advanced Graph Neural Networks被引用 8

一句话总结

这篇论文提出一种知识图谱实体提取与关系推理框架，使用图卷积网络和注意力网络构建端到端模型，用于在复杂知识图谱中的实体识别和关系推断。

ABSTRACT

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.

研究动机与目标

• 说明在复杂知识图谱中需要有效的实体提取与关系推理的必要性。
• 开发一个端到端模型，联合执行实体识别和关系推理。
• 利用图卷积网络（GCN）和图注意力网络（GAT）来捕捉复杂KG结构。

提出的方法

• 构建一个端到端的联合模型，结合 GCN 和 GAT 来建模KG结构。
• 应用基于图的表示来提取实体并推断关系。
• 使用 AUC、召回率、精确率和 F1 等指标，与各种深度学习基线进行对比评估。

实验结果

研究问题

• RQ1 基于图神经网络的框架能否提高复杂KG中实体提取的准确性和鲁棒性？
• RQ2 实体提取与关系推理的联合建模是否优于模块化方法？
• RQ3 GCN 和 GAT 组件如何促进在复杂知识图中的性能与泛化？

主要发现

• 所提出的模型在 AUC、召回率、精确率和 F1 等指标上均优于基线。
• 该模型在复杂知识图中表现出强泛化性和稳定性。
• 结果支持图神经网络在 KG 应用中的实体提取和关系推理潜力。