基于投影变换的5* 知识图谱嵌入
5* Knowledge Graph Embeddings with Projective Transformations
摘要：
使用知识图谱嵌入模型进行链接预测已经成为知识图谱补全的流行方法。这样的模型采用变换函数，该变换函数经由边将节点映射到向量空间中，以便测量链接的可能性。在映射单个节点的同时，子图的结构也发生了变化。大多数在欧几里德几何中设计的嵌入模型通常支持单一的变换类型-通常是平移或旋转，这适合在相邻子图差异很小的图上学习。然而，多关系知识图谱通常在邻域中包括多个子图结构（例如，路径和循环结构的组合），当前嵌入模型不能很好地捕捉这些子图结构。为了解决这个问题，我们提出了一种新的KGE模型（5*E）在射影几何，它支持多个同时变换-具体的反转，反射，平移，旋转和homothety。该模型有几个有利的理论性质，并纳入现有的方法。它在最广泛使用的链接预测基准测试中的表现优于它们
英文摘要：
Performing link prediction using knowledge graph embedding models has become a popular approach for knowledge graph completion. Such models employ a transformation function that maps nodes via edges into a vector space in order to measure the likelihood of the links. While mapping the individual nodes, the structure of subgraphs is also transformed. Most of the embedding models designed in Euclidean geometry usually support a single transformation type - often translation or rotation, which is suitable for learning on graphs with small differences in neighboring subgraphs. However, multi-relational knowledge graphs often include multiple sub-graph structures in a neighborhood (e.g. combinations of path and loop structures), which current embedding models do not capture well. To tackle this problem, we propose a novel KGE model (5*E) in projective geometry, which supports multiple simultaneous transformations - specifically inversion, reflection, translation, rotation, and homothety. The model has several favorable theoretical properties and subsumes the existing approaches. It outperforms them on the most widely used link prediction benchmarks
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
