Weighted Aggregator for the Open-World Knowledge Graph Completion  

作　　者：Yueyang Zhou Shumin Shi Heyan Huang 

机构地区：[1]School of Computer Science and Technology,Beijing Institute of Technology,Beijing,China

出　　处：《国际计算机前沿大会会议论文集》2020年第1期283-291,共9页International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）

基　　金：the National Natural Science Foundation of China(Grant No.61671064,No.61732005);National Key Research&Development Program(Grant No.2018YFC0831700).

摘　　要：Open-world knowledge graph completion aims to find a set of missing triples through entity description,where entities can be either in or out of the graph.However,when aggregating entity description’s word embedding matrix to a single embedding,most existing models either use CNN and LSTM to make the model complex and ineffective,or use simple semantic averaging which neglects the unequal nature of the different words of an entity description.In this paper,an aggregator is proposed,adopting an attention network to get the weights of words in the entity description.This does not upset information in the word embedding,and make the single embedding of aggregation more efficient.Compared with state-of-the-art systems,experiments show that the model proposed performs well in the open-world KGC task.

关 键 词：OPEN LSTM EMBEDDING 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]