Transcription of Knowledge Graph Embedding via Dynamic Mapping Matrix
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Knowledge Graph Embedding via Dynamic Mapping Matrix
Knowledge Graph Embedding via Dynamic Mapping Matrix Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu and Jun Zhao National Laboratory of Pattern Recognition (NLPR). Institute of Automation Chinese Academy of Sciences, Beijing, 100190, China Abstract completion is to predict relations between entities based on existing triplets in a Knowledge Graph . In Knowledge graphs are useful resources for the past decade, much work based on symbol and numerous AI applications, but they are far logic has been done for Knowledge Graph comple- from completeness. Previous work such as tion, but they are neither tractable nor enough con- TransE, TransH and TransR/CTransR re- vergence for large scale Knowledge graphs.
knowledge graph into a low-dimensional embed-ding vector space. These methods do reasoning over knowledge graphs through algebraic opera-tions (see section Related Work ). Among these methods, TransE (Bordes et al. 2013) is simple and effective, and also achieves state-of-the-art prediction performance. It learns
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