论文标题
知识关联与双曲知识图嵌入
Knowledge Association with Hyperbolic Knowledge Graph Embeddings
论文作者
论文摘要
通过实体对齐，实体类型推理和其他相关任务捕获知识图（kgs）的关联，使NLP应用程序具有全面的知识表示。基于欧几里得嵌入的最新相关方法受到层次结构和不同量表的挑战。它们还取决于高嵌入尺寸以实现足够的表现力。不同的是，我们用低维的双曲线嵌入知识关联探索。我们提出了一个双曲线关系图神经网络，用于KG嵌入，并捕获具有双曲线转换的知识关联。关于实体比对和类型推断的广泛实验证明了我们方法的有效性和效率。
Capturing associations for knowledge graphs (KGs) through entity alignment, entity type inference and other related tasks benefits NLP applications with comprehensive knowledge representations. Recent related methods built on Euclidean embeddings are challenged by the hierarchical structures and different scales of KGs. They also depend on high embedding dimensions to realize enough expressiveness. Differently, we explore with low-dimensional hyperbolic embeddings for knowledge association. We propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a hyperbolic transformation. Extensive experiments on entity alignment and type inference demonstrate the effectiveness and efficiency of our method.
