ITM Web Conf.
Volume 12, 2017The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|Number of page(s)||5|
|Section||Session 4: Information Theory and Information Systems|
|Published online||05 September 2017|
Knowledge Reasoning Based on Neural Tensor Network
Massive Data Processing Lab National University of Defense Technology, Changsha, China
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 increasing 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.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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