基于生成对抗网络与渐进式融合的多模态实体对齐

打开文本图片集

Multimodal entity alignment based on dual-generator shared-adversarial network

Feng Guanga†，Zheng Runtingʰ，Liu Tianxiangʰ，Yang Yanruʰ，Lin Jianzhonga， Zhong Tinga，HuangRongcanʰ，XiangFengʰ，LiWeichenb （a.SchoolofAutomation，b.SchoolofComputerScience，Guangdong UniversityofTechnology，Guangzhou510o6，China）

Abstract：Inthefieldofeducation，knowledgegraph fusionplaysacrucialrole.Asacore technologyof knowledge graphfusion，entityalignmentaistoidentifyequivalent entitypairsacrossmultiple knowledge graphs.Most existing entityalignment methodsassume thateachsourceentityhasacorresponding entityinthetargetknowledge graph.However，whenusingcrosslingualandcros-raphetitysets，theproblemofdanglingentitiesarises.Toaddresstisissue，thispaperproposedthedualgeneratorshared-adversarial network entityalignment model（DGSAN-EA）.This modelutilized partialparametersharig and anoptimalselectionstrategytotraintwogenerators，selectingtheoptimalgneatortoconditionallgenerateewetisacoss knowledgegraphs，therebyenhancing thedatasetand solving thedangling entityproblem.Furthermore，aprogressive fusion strategyandtheintroductionofdistributionconsistencylossfunctionefectielyresolvethedistortionoffusedfeatureformationandthemisalignmentbetweenmodalitiesinmultimodalentityalignment.Validationonmultiplepublicdatasetsshows that compared to existing multimodal entityalignment models，DGSAN-EAachieveshigher hit@ kand MMRscores，demonstrating itseffectiveness in entity alignment tasks.

Key words：knowledge graph（KG）;entity alignment;adversarial network；dual generator;parameter sharing；progresive fusion；distribution consistency

0 引言

在大数据时代背景下，知识图谱（KG）作为结构化知识表示的重要工具，其应用已扩展至教育、医疗、金融等关键领域。（剩余22678字）