基于TransE的表示学习方法研究综述

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2020-09-28 Cooperative journals: 《计算机应用研究》

张正航 钱育蓉 行艳妮 赵鑫

Abstract： In order to understand the latest research progress of TransE based representation learning methods in real time, this paper classifies TransE based representation learning methods into four types: the method based on complex relationship, the method based on relationship path, the method based on image information, and the method based on other aspects. Then, this paper analyzes the design ideas, advantages and disadvantages of each method. At the same time, it compares and summarizes the common data sets and evaluation indexes of the TransE based representation learning method, as well as the performance of various TransE based representation learning algorithms in the experiment. Finally, this paper summarizes the research of the whole paper and looks forward to the future research hotspot. From the research results, PaSKoGE method, NTransGH method, TCE method and TransD method perform the best in link prediction and triple classification tasks, which are worth promoting and further expanding, and can be further improved in path specific embedding, two-layer neural network, triple context and dynamic mapping matrix construction.

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