Knowledge Reasoning Based on Neural Tensor Network

Jian-Hui Huang, Jiu-Ming Huang, Ai-Ping Li, Yong-Zhi Tong, L. Long, Y. Li, X. Li, Y. Dai, H. Yang
2017 ITM Web of Conferences  
Knowledge base (KBs) is a very important part of applications such as Q&A system, but the knowledge base is always faced with incompleteness and the lack of inter-entity relationships. Knowledge reasoning is an important part of the construction of knowledge base, and is intended to find a way to supplement these missing relationships. This paper attempts to explore the model complexity of neural tensor network, a very important method of knowledge reasoning, and the reasoning accuracy. By
more » ... asing the number of slices in the tensor network layer, the number of parameters to be trained by the model is increased, thereby increasing the complexity of the model. The experimental results show that the number of slices is improved, which is helpful to increase the reasoning accuracy of the model, while the time consumption does not show obvious growth. The accuracy of the model on WordNet and FreeBase increased 2% and 3.2% respectively.
doi:10.1051/itmconf/20171204004 fatcat:jnh7dlb6xfd63jdkuz6lm4euba