Research on link prediction model based on hierarchical attention mechanism

In order to solve the problem that the existing graph attention mechanism tends to cause attention distribution to certain relations with high frequency when performing link prediction related tasks, a new link prediction model based on hierarchical attention mechanism was proposed.In the link predi...

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Main Authors: Xiaojuan ZHAO, Yan JIA, Aiping LI, Kai CHEN
Format: Article
Language:zho
Published: Editorial Department of Journal on Communications 2021-03-01
Series:Tongxin xuebao
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Online Access:http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021057/
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author Xiaojuan ZHAO
Yan JIA
Aiping LI
Kai CHEN
author_facet Xiaojuan ZHAO
Yan JIA
Aiping LI
Kai CHEN
author_sort Xiaojuan ZHAO
collection DOAJ
description In order to solve the problem that the existing graph attention mechanism tends to cause attention distribution to certain relations with high frequency when performing link prediction related tasks, a new link prediction model based on hierarchical attention mechanism was proposed.In the link prediction task, a hierarchical attention mechanism was designed to give different attention to the relationships of different relationship types connected to a given entity in the knowledge graph according to the relationship in the prediction task.While the characteristics of multi-hop neighbor entities were pay attention to, the relationship characteristics was pay more attention to find the relationship type that matches the target relationship.Through comparison experiments with the mainstream models on multiple benchmark data sets, the results show that the performance of the model is better than the mainstream models and has good robustness.
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publisher Editorial Department of Journal on Communications
record_format Article
series Tongxin xuebao
spelling doaj-art-cbc11048b6a042fdbd047e49a8abda662025-08-20T02:34:39ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-03-0142364459740616Research on link prediction model based on hierarchical attention mechanismXiaojuan ZHAOYan JIAAiping LIKai CHENIn order to solve the problem that the existing graph attention mechanism tends to cause attention distribution to certain relations with high frequency when performing link prediction related tasks, a new link prediction model based on hierarchical attention mechanism was proposed.In the link prediction task, a hierarchical attention mechanism was designed to give different attention to the relationships of different relationship types connected to a given entity in the knowledge graph according to the relationship in the prediction task.While the characteristics of multi-hop neighbor entities were pay attention to, the relationship characteristics was pay more attention to find the relationship type that matches the target relationship.Through comparison experiments with the mainstream models on multiple benchmark data sets, the results show that the performance of the model is better than the mainstream models and has good robustness.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021057/hierarchical attention mechanismlink predictionknowledge graph embedding
spellingShingle Xiaojuan ZHAO
Yan JIA
Aiping LI
Kai CHEN
Research on link prediction model based on hierarchical attention mechanism
Tongxin xuebao
hierarchical attention mechanism
link prediction
knowledge graph embedding
title Research on link prediction model based on hierarchical attention mechanism
title_full Research on link prediction model based on hierarchical attention mechanism
title_fullStr Research on link prediction model based on hierarchical attention mechanism
title_full_unstemmed Research on link prediction model based on hierarchical attention mechanism
title_short Research on link prediction model based on hierarchical attention mechanism
title_sort research on link prediction model based on hierarchical attention mechanism
topic hierarchical attention mechanism
link prediction
knowledge graph embedding
url http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021057/
work_keys_str_mv AT xiaojuanzhao researchonlinkpredictionmodelbasedonhierarchicalattentionmechanism
AT yanjia researchonlinkpredictionmodelbasedonhierarchicalattentionmechanism
AT aipingli researchonlinkpredictionmodelbasedonhierarchicalattentionmechanism
AT kaichen researchonlinkpredictionmodelbasedonhierarchicalattentionmechanism