融合實(shí)體鄰域信息的時序知識圖譜實(shí)體對齊

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關(guān)鍵詞：時序知識圖譜；實(shí)體對齊；知識嵌入；實(shí)體活躍度；潛在鄰居 中圖分類號：TP391.1 文獻(xiàn)標(biāo)志碼：A 文章編號：1001-3695（2025）07-017-2048-08 doi：10.19734/j. issn.1001-3695.2024.12.0513

Abstract：Thepurposeof entityalignment is tobuildacomprehensive knowledgegraphbymatching identical entitiesacross multi-source knowledge graphs.Existing methods primarilyfocus onstaticknowledge graphs，failing tofulluizetheabundanttemporal informationpresentinlarge-scaleknowledgestructures，andneglectingpotentialneighbors thatarenotdirectly conectedwhileintegratingentityneighborhood information.Toaddressthesechallenges，thispaperproposedanentityalignmentmodel，EA，totegateighborhoodiformationintomporalnowledgeaphs.irstly，itonstructede ralfeaturesofentitiesbycapturingtheiractivitiesatdiferentimepoints.Next，itintroducedaneighborhoodsimilaritymeasurement methodto identifypotential neighboring nodes，enabling message pasing acrossseparatechannelstoextractdiverse neighborhoodrepresentationsandcapturedomain-specificfeatures.Finallyitcombined the temporalandneighborhoodfeatures of ntitiestogenerateenrichedentityembeddings.Experimentalresultsdemonstratethatthe proposed modelimproves hits@1 by1.8，1.7，1.7，and1.9 percentagepointson fourreal-worlddatasets，DICEWS-1K/20andYAGO-WIKI50K5K/1K，compared tothebest-performing baselinemodels.Thesefindings validatetheefectivenessof the proposedapproach. Key words：temporal knowledge graph;entity alignment;knowledge embeddng;entity activity；potential neighbors

0 引言

知識圖譜（knowledgegraph，KG）[1]以圖結(jié)構(gòu)的形式存儲知識，圖中的節(jié)點(diǎn)表示實(shí)體，節(jié)點(diǎn)之間的邊表示關(guān)系。（剩余19392字）