论文标题
复合：知识图嵌入具有翻译，旋转和缩放化合物操作的嵌入
CompoundE: Knowledge Graph Embedding with Translation, Rotation and Scaling Compound Operations
论文作者
论文摘要
翻译，旋转和缩放是图像处理中的三个常用几何操纵操作。此外，其中一些成功用于开发有效的知识图嵌入（KGE）模型，例如Transe和旋转。受协同作用的启发，我们通过利用这项工作中的所有三项操作提出了一种新的KGE模型。由于翻译，旋转和缩放操作被级联形成复合，因此新模型被命名为复合。通过在小组理论框架中铸造复合物，我们表明，基于得分功能的基于功能的kge模型是复合的特殊情况。 Compounde将简单的基于距离的关系扩展到与关系有关的化合物操作上的头部和/或尾部实体。为了证明化合物的有效性，我们在三个流行的KG完成数据集上进行实验。实验结果表明，复合者始终达到了现状的性能。
Translation, rotation, and scaling are three commonly used geometric manipulation operations in image processing. Besides, some of them are successfully used in developing effective knowledge graph embedding (KGE) models such as TransE and RotatE. Inspired by the synergy, we propose a new KGE model by leveraging all three operations in this work. Since translation, rotation, and scaling operations are cascaded to form a compound one, the new model is named CompoundE. By casting CompoundE in the framework of group theory, we show that quite a few scoring-function-based KGE models are special cases of CompoundE. CompoundE extends the simple distance-based relation to relation-dependent compound operations on head and/or tail entities. To demonstrate the effectiveness of CompoundE, we conduct experiments on three popular KG completion datasets. Experimental results show that CompoundE consistently achieves the state of-the-art performance.
