基于句子转换和双注意力机制的归纳关系预测

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Inductive relation prediction based on sentence Transformer and dual attention mechanism

Li Weijun a，b† ，Liu Xueyang²，Liu Shixia，Wang Ziyia，Ding Jianpinga，Su Yileia （a.Colegeofuene&oKbafgs&asellgentossnofateEcs sion，North Minzu University，Yinchuan75oo21，China）

Abstract：Relation predictionisaimportant task in knowledgegraphcompletion，aimedat predicting mising relationships between entities.Existing inductiverelationprediction methodsoften facechalenges inadequatelymodeling semanticand structural information.Toaddress thisisse，thispaper proposedaninductiverelation predictionmodel basedonsentence transformationandadual-atentionmechanism.Theproposedmethodenhancedentitysemanticrepresentationsbyautomaticallyretrievingdescritionsandincorporatesadual-atentionmechanism，whichonsiderededgeandelationawarenes，toacu ratelymodelthecomplexinteractionsbetweeentities.Firstly，itextractedtheclosedsubgraphofthetargettripleanduseda random walk strategytosearchformuli-hoprelational paths.Thesetriplesand pathswerethen transformedinto natural language sentences，generating semanticall rich sentence embeddings.Next，it updated the subgraph embeddings using GCN andbidirectionalGRU，combiningsentenceandsubgraphembeddings tocapturebothstructuralandsemanticinformationExperimentalresultsonthree public datasets—WN18RR，FB15k-237，andNELL-995—demonstratethattheproposed method outperformsexisting methodsinbothtransformationandinductiverelationpredictiontasks，validatingtheimportanceof the dual-attention mechanismandsentence transformationin improving model performance.Thisapproach efectively enhances the accuracy and efficiency of relation prediction in knowledge graphs.

Key words：knowledge graph（KG）；inductiverelation prediction；sentence Transformer;dual atention mechanism；random walk pathfinding strategy

0 引言

知识图谱（KG）是一种用于表示和存储客观世界知识的图形化知识库，通过三元组方式进行形式化描述，广泛应用于网络搜索[1]、社区检测[2]和推荐系统[3]等任务。（剩余17867字）