论文标题
5*具有投射转换的知识图嵌入
5* Knowledge Graph Embeddings with Projective Transformations
论文作者
论文摘要
使用知识图嵌入模型执行链接预测已成为知识图完成的流行方法。这样的模型采用转换函数，该函数通过边缘将节点映射到矢量空间中，以测量链接的可能性。在映射各个节点时，也会转换子图的结构。欧几里得几何形状设计的大多数嵌入模型通常支持单个转换类型 - 通常翻译或旋转，这适用于在相邻子图中差异很小的图表上学习。但是，多关系知识图通常包括邻域中的多个子图结构（例如，路径和环结构的组合），当前嵌入模型无法很好地捕获。为了解决这个问题，我们提出了一个新颖的KGE模型（5*e），该模型支持多个同时转换，特别是反转，反射，翻译，旋转，旋转和同型。该模型具有几种有利的理论属性，并涵盖了现有方法。它在最广泛使用的链接预测基准上胜过它们
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
