论文标题
对齐嵌入空间是一项具有挑战性的任务吗？一项关于异质嵌入比对方法的研究
Is Aligning Embedding Spaces a Challenging Task? A Study on Heterogeneous Embedding Alignment Methods
论文作者
论文摘要
将单词和知识图（kg）的表示形式学习到低维矢量空间及其在许多现实世界情景中的应用，最近已经获得了动力。为了利用多个kg嵌入知识驱动的应用程序，例如问答，命名实体歧义歧义，知识图完成等，需要对不同kg嵌入空间的对齐。除了多语言和特定于领域的信息外，不同的kg还提出了结构差异的问题，这使得kg嵌入的对齐变得更具挑战性。本文提供了三个代表实体实体和实体字的嵌入空间之间的最新对准方法的理论分析和比较。本文还旨在以不同的应用程序的借口评估现有对齐方式的能力和缩写。
Representation Learning of words and Knowledge Graphs (KG) into low dimensional vector spaces along with its applications to many real-world scenarios have recently gained momentum. In order to make use of multiple KG embeddings for knowledge-driven applications such as question answering, named entity disambiguation, knowledge graph completion, etc., alignment of different KG embedding spaces is necessary. In addition to multilinguality and domain-specific information, different KGs pose the problem of structural differences making the alignment of the KG embeddings more challenging. This paper provides a theoretical analysis and comparison of the state-of-the-art alignment methods between two embedding spaces representing entity-entity and entity-word. This paper also aims at assessing the capability and short-comings of the existing alignment methods on the pretext of different applications.
