%A Chuanming Yu,Haonan Li,Manyi Wang,Tingting Huang,Lu An %T Knowledge Representation Based on Deep Learning:Network Perspective %0 Journal Article %D 2020 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2019.0505 %P 63-75 %V 4 %N 1 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4762.shtml} %8 2020-01-25 %X

[Objective] This paper explores better representation models for the semantic relationship among knowledge objects.[Methods] Based on the existing algorithm of network representation learning, we proposed a combined knowledge network representation learning model (CKNRL), with integrated learning and deep learning techniques.[Results] We examined our new model with the knowledge network link prediction task of Chinese and English news parallel corpus. The AUC value of the CKNRL model was 0.929, which was higher than those of the traditional algorithms, i.e. DeepWalk(0.925), Node2Vec(0.926) and SDNE(0.899).[Limitations] Our study was based on the word co-occurrence network, and more research is needed to examine the CKNRL model for link prediction on more types of knowledge networks.[Conclusions] The semantic relationship among knowledge objects can be better represented by the proposed fusion model.