Boosting Cross-lingual Entity Alignment with Textual Embedding

摘要：

　　Multilingual knowledge graph(KG)embeddings have attracted many researchers,and benefit lots of cross-lingual tasks.The cross-lingual entity alignment task is to match equivalent entities in dif-ferent languages,which can largely enrich the multilingual KGs.Many previous methods consider solely the use of structures to encode enti-ties.However,lots of multilingual KGs provide rich entity descriptions.In this paper,we mainly focus on how to utilize these descriptions to boost the cross-lingual entity alignment.Specifically,we propose two textual embedding models called Cross-TextGCN and Cross-TextMatch to embed description for each entity.Our experiments on DBP15K show that these two textual embedding model can indeed boost the structure based cross-lingual entity alignment model.

关键词： Cross-lingual entity alignment Graph Convolutional Networks Entity embedding.

作者: Wei Xu Chen Chen Chenghao Jia Yongliang Shen Xinyin Ma Weiming Lu

作者单位: College of Computer Science and Technology,Zhejiang University,Hangzhou,China

会议类型: 国际会议

会议名称: 9th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC 2020)

会议地点: 郑州

会议语种:英文

页码: 1057-1069

在线出版日期: 2020-10-14（万方平台首次上网日期，不代表论文的发表时间）