A Generative Adversarial Negative Sampling Method for Knowledge Hypergraph Link Prediction
-
摘要: 知识超图作为知识图谱的拓展，对多元关系事实具有良好表达能力.利用知识超图对现实世界中已知事实进行建模，并通过链接预测发现未知事实成为当前研究热点.在现有知识超图(知识图谱)链接预测方法中，构建样本真实标签与预测标签间的损失函数是关键步骤，其中负样本对链接预测模型的训练具有极大的影响.将知识图谱链接预测的负采样方法(如均匀随机负采样)用于知识超图链接预测会面临负样本质量低下、复杂度过高等问题.对此，设计了面向知识超图链接预测的生成对抗负采样方法HyperGAN，通过对抗训练生成高质量负样本以解决“零损失”问题，从而提升链接预测模型的准确度.HyperGAN方法无需预训练，因此在辅助链接预测模型进行训练时相比现有负采样方法具有更高的效率.在多个真实数据集上的对比实验表明：HyperGAN在性能与效率方面均优于基线方法.此外，具体案例分析及定量分析亦验证了HyperGAN方法在提升负样本质量方面的有效性.Abstract: As an extension of the knowledge graph, the knowledge hypergraph has a strong ability to express n-ary relational facts. Using the knowledge hypergraph to model known facts in the real world and discover unknown facts through link prediction has become a current research hotspot. Among existing knowledge hypergraph (or knowledge graph) link prediction methods, constructing the loss function using true labels of samples and their predicted labels is a key step, where negative samples have a great influence on the training of the link prediction model. However, when applying the negative sampling methods for knowledge graph link prediction (e.g., the uniformly random sampling) to the knowledge hypergraph, we may face problems such as low quality of negative samples and high complexity of models. As a result, we design a generative adversarial negative sampling method, named HyperGAN, for knowledge hypergraph link prediction, which generates high-quality negative samples through adversarial training to solve the zero loss problem, thereby improving the accuracy of the link prediction model. Besides, HyperGAN does not require pre-training, which makes it more efficient than previous negative sampling methods in assisting the training of link prediction models. Comparative experiments on multiple real-world datasets show that HyperGAN outperforms the baselines in terms of performance and efficiency. In addition, the case study and quantitative analysis further validate our method in improving the quality of negative samples.
-
-
期刊类型引用(1)
1. 王伟，杜旭洋，黄平，史高峰. 基于地图匹配的辅助定位算法研究. 电子测量技术. 2023(23): 14-19 . 
百度学术
其他类型引用(3)
计量
- 文章访问数: 200
- HTML全文浏览量: 12
- PDF下载量: 136
- 被引次数: 4
下载:
