LGHAE: Local and Global Hyper-relation Aggregation Embedding for Link Prediction  

作　　者：Peikai Yuan Zhenheng Qi Hui Sun Chao Liu 

机构地区：[1]College of Computer Science and Technology,Harbin Engineering University,Harbin,China [2]Institute of Systems Engineering,Academy of Military Sciences,Beijing 100089,China

出　　处：《国际计算机前沿大会会议论文集》2023年第2期364-378,共15页International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）

摘　　要：The Knowledge Graph(KGs)have profoundly impacted many researchfields.However,there is a problem of low data integrity in KGs.The binary-relational knowledge graph is more common in KGs but is limited by less information.It often has less content to use when predicting missing entities(relations).The hyper-relational knowledge graph is another form of KGs,which introduces much additional information(qualifiers)based on the main triple.The hyper-relational knowledge graph can effectively improve the accuracy of pre-dicting missing entities(relations).The existing hyper-relational link prediction methods only consider the overall perspective when dealing with qualifiers and calculate the score function by combining the qualifiers with the main triple.How-ever,these methods overlook the inherent characteristics of entities and relations.This paper proposes a novel Local and Global Hyper-relation Aggregation Embed-ding for Link Prediction(LGHAE).LGHAE can capture the semantic features of hyper-relational data from local and global perspectives.To fully utilize local and global features,Hyper-InteractE,as a new decoder,is designed to predict missing entities to fully utilize local and global features.We validated the feasibility of LGHAE by comparing it with state-of-the-art models on public datasets.

关 键 词：Knowledge Graph Hyper-relation Link Prediction Knowledge Graph Completion 

分 类 号：O15[理学—数学]