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一种融合实体语义知识的实体集合扩展方法.docx

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一种融合实体语义知识的实体集合扩展方法 Title:EntitySetExpansion:AMethodforIncorporatingEntitySemanticKnowledge 1.Introduction: Inrecentyears,theprogressinnaturallanguageprocessingandmachinelearningtechniqueshasenabledtheextractionandutilizationofsemanticknowledgefromvarioussources,suchasontologies,knowledgegraphs,andsemanticwebdata.Entitysetexpansionisataskthataimstoexpandagivenentitysetbyincorporatingadditionalentitiesthataresemanticallyrelated.Thispaperproposesanovelmethodforentitysetexpansionthatleveragesentitysemanticknowledgetoenhancetheprocess. 2.RelatedWork: Previousapproachestoentitysetexpansionhavefocusedonvariousmethods,includingkeyword-basedtechniques,similaritymeasures,andgraph-basedalgorithms.Whilethesemethodshaveshownpromisingresults,theyoftensufferfromlimitedcoverage,lackofsemanticunderstanding,orinabilitytohandlenoisyorincompletedata.Toaddresstheselimitations,thisresearchproposesamethodthateffectivelyintegratesentitysemanticknowledgeintotheexpansionprocess. 3.Methodology: Theproposedentitysetexpansionmethodconsistsofthefollowingkeysteps: 3.1EntitySemanticKnowledgeExtraction: Toincorporateentitysemanticknowledge,weextractrelevantinformationfromexistingresourcessuchasontologies,KnowledgeGraphs(KGs),andsemanticwebdata.Thisinvolvesleveragingtechniqueslikenamedentityrecognition,entitydisambiguation,andontologyalignmenttoensureaccurateandcomprehensiverepresentationofentitiesandtheirrelationships. 3.2RepresentationLearning: Next,weemployrepresentationlearningtechniques,suchaswordembeddingsorgraph-basedembeddings,toencodetheextractedentitysemanticknowledgeintoacompactandmeaningfulrepresentation.Thisstepensuresthatallrelevantinformationaboutentitiesandtheirrelationshipsiscapturedinaformsuitableforsubsequentanalysis. 3.3SimilarityEstimation: Basedonthelearnedrepresentations,wecalculatethesimilaritybetweentheentitiesinthegivenentitysetandotherentitiesfoundintheexternalknowledgesources.Varioussimilaritymeasures,suchascosinesimilarityorgraphsimilarity,canbeemployedtoquantifythesema