%A YU Yuan-yuan, CHAO Wen-han, HE Yue-ying, LI Zhou-jun %T Cross-language Knowledge Linking Based on Bilingual Topic Model and Bilingual Embedding %0 Journal Article %D 2019 %J Computer Science %R 10.11896/j.issn.1002-137X.2019.01.037 %P 238-244 %V 46 %N 1 %U {https://www.jsjkx.com/CN/abstract/article_17764.shtml} %8 2019-01-15 %X Cross-language knowledge linking (CLKL) refers to the establishment of links between encyclopedia articles in different languages that describe the same content.CLKL can be divided into two parts:candidate selection and candidate ranking.Firstly,this paper formulated candidate selection as cross-language information retrieval problem,and proposed a method to generate query by combining title with keywords,which greatly improves the recall of candidate selection,reaching 93.8%.In the part of the candidate ranking,this paper trained a ranking model by mixing bilingual topic model and bilingual embedding,implementing military articles linking in English Wikipedia and Chinese Baidu Baike.The evaluation results show that the accuracy of model achieves 75%,which significantly improves the perfor-mance of CLKL.The proposed method does not depend on linguistic characteristics and domain characteristics,and it can be easily extended to CLKL in other languages and other domains.