Computer Science ›› 2020, Vol. 47 ›› Issue (4): 189-193.doi: 10.11896/jsjkx.190300024

• Artificial Intelligence • Previous Articles     Next Articles

Knowledge Graph Representation Based on Improved Vector Projection Distance

LI Xin-chao, LI Pei-feng, ZHU Qiao-ming   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,ChinaProvincial Key Laboratory for Computer Information Processing Technology,Suzhou,Jiangsu 215006,China
  • Received:2019-03-08 Online:2020-04-15 Published:2020-04-15
  • Contact: LI Pei-feng,born in 1971,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include natural language processing and machine learning.
  • About author:LI Xin-chao,born in 1995,postgradua-te.His main research interests include natural language processing and representation learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61836007,61772354,61773276).

Abstract: Representation learning is of great value in knowledge graph reasoning,which realizes the computability of knowledge by embedding entities and relationships into a low-dimensional space.The representation learning model based on vector projection distance has better ability of knowledge representation on complex relationships.However,the model is easily susceptible to irrelevant information,especially when dealing with one-to-one relationships,and it still has space to improve performance in representing one-to-many,many-to-one and many-to-many relationships.In this paper,we proposed an improved representation learning model SProjE,which introduces an adaptive metric method to reduce the weight of noise information and optimizes the loss function to improve the loss weight of complex relation triples.The proposed model is suitable for large scale knowledge graph representation learning.At last,the experimental results on the WN18 and FB15k data sets show that SProjE achieves significant and consistent improvements compared with the existing models and methods.

Key words: Knowledge graph, Representation learning, Adaptive metric, Entity link prediction

CLC Number: 

  • TP391.1
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