论文标题
卷积复杂知识图嵌入
Convolutional Complex Knowledge Graph Embeddings
论文作者
论文摘要
在本文中，我们研究了学习知识图的连续矢量表示的问题，以预测缺失的链接。我们提出了一种名为Conex的新方法，该方法通过利用与复杂值嵌入载体的Hermitian内部产物的组成来散布缺少链接。我们根据WN18RR，FB15K-237，亲属关系和UMLS基准数据集对CONEX进行评估。我们的实验结果表明，CONEX在所有数据集上的链接预测任务上的旋转，贵族和塔克等最先进的方法的表现优于最先进的性能，同时需要少8倍的参数。我们通过提供开源实现，包括培训，评估脚本以及https://github.com/conex-kge/conex的预培训模型，确保结果的可重复性。
In this paper, we study the problem of learning continuous vector representations of knowledge graphs for predicting missing links. We present a new approach called ConEx, which infers missing links by leveraging the composition of a 2D convolution with a Hermitian inner product of complex-valued embedding vectors. We evaluate ConEx against state-of-the-art approaches on the WN18RR, FB15K-237, KINSHIP and UMLS benchmark datasets. Our experimental results show that ConEx achieves a performance superior to that of state-of-the-art approaches such as RotatE, QuatE and TuckER on the link prediction task on all datasets while requiring at least 8 times fewer parameters. We ensure the reproducibility of our results by providing an open-source implementation which includes the training, evaluation scripts along with pre-trained models at https://github.com/conex-kge/ConEx.
