Journal of Guangdong University of Technology ›› 2022, Vol. 39 ›› Issue (04): 24-31.doi: 10.12052/gdutxb.210062

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A Knowledge Representation Model Based on Bi-Objective Alternate Optimization Under Probability

Zhang Xin, Wang Zhen-you   

  1. School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510520, China
  • Received:2021-04-25 Online:2022-07-10 Published:2022-06-29

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Abstract: Aiming at the problem that the TransD model has many parameters and the two representations of entities are not related, an improved knowledge representation model PTransD is proposed, which reduces the number of parameters by reducing the number of entity projections and clustering entities, while using K-L (Kullback-Leibler ) The divergence limits the entity projection to the same probability distribution as the corresponding entity class. During model training, the triple loss and K-L loss are alternately optimized, and the entities in the classes with large spacing between the entities are replaced to improve the quality of negative examples. Finally, based on the experimental results of triple classification and link prediction on the knowledge graph data set, the performance has been significantly improved in various indicators. It can be applied to the perfection and reasoning of knowledge map.

Key words: knowledge graph, representation learning, alternate optimization, triple classification, link prediction

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  • TP3
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