Knowledge Graph Embedding Technology: A Review

Abstract

Knowledge graph embedding (KGE) is a new research hotspot in the field of knowledge graphs, which aims to apply the translation invariance of word vectors to embedding entities and relationships of the knowledge graph into a low-dimensional vector space to complete knowledge representation. In this paper, it is mainly concerned with the classification according to the types of practical problems to be solved. Firstly, it expounds four major types of embedding methods of knowledge graph, including deep learning-based methods, graphical features-based methods, translation model-based methods, and other model-based methods. The algorithm ideas of each model are elaborated, and the advantages and disadvantages of each model are concluded. Secondly, the algorithm experi-ment of knowledge graph embedding is analyzed and summarized from the four aspects of commonly used data sets, evaluation indicators, algorithms, and experiments, then a horizontal and vertical comparison of the embedding method is made. Finally, from the perspective of solving practical problems, the future direction of knowledge graph embedding technology is given. Through research, it is discovered that in the deep learning-based method, LCPE achieves the best effect; in the graphical features-based method, TCE makes the best impression; whereas in the translation model-based method, NTransGH responds most optimistically. Future researches can be expanded on the basis of LCPE, TCE, and NTransGH to continuously improve the experimental effects of link prediction and triplets classification.

Keywords

• knowledge graph embedding (kge)
• knowledge representation
• knowledge graph completion (kgc)
• link prediction; triple classification