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一种基于知识的信息检索方法 Title:AKnowledge-basedInformationRetrievalMethod Abstract: Informationretrievalisacriticaltaskinthefieldofcomputerscience.Withthegrowingamountofdigitalinformationavailable,itbecomesincreasinglychallengingtoretrieverelevantandaccurateinformationefficiently.Inthispaper,weproposeaknowledge-basedinformationretrievalmethodthatutilizesknowledgegraphstoenhancethesearchprocess.Wediscussthecomponentsofthismethod,includingknowledgeacquisition,knowledgerepresentation,andknowledgeutilization.Wealsopresentacasestudytodemonstratetheeffectivenessofourapproach.Theresultsindicatethattheproposedmethodleadstoimprovedaccuracyandefficiencyininformationretrievaltasks. 1.Introduction Informationretrieval(IR)istheprocessofextractingrelevantinformationfromalargeandunstructuredcollectionofdata.TraditionalIRmethodsprimarilyrelyonkeywordmatchingtechniques,whichoftensufferfromlimitationssuchasambiguityandlackofsemanticunderstanding.Inrecentyears,advancementsinknowledgerepresentationandmachinelearninghavepavedthewayfornewapproachesthatleverageknowledgetoimprovetheretrievalprocess.Thispaperpresentsaknowledge-basedinformationretrievalmethodthatincorporatesknowledgegraphstoenhancesearcheffectiveness. 2.KnowledgeAcquisition Knowledgeacquisitionisacrucialstepinbuildingaknowledge-basedinformationretrievalsystem.Itinvolvesextractingknowledgefromvarioussources,suchastextbooks,scientificarticles,andwebpages.NaturalLanguageProcessing(NLP)techniques,suchasnamedentityrecognitionandrelationextraction,canbeappliedtoautomaticallyextractstructuredknowledgefromunstructuredtext.Additionally,existingknowledgebases,suchasWikipediaordomain-specificontologies,canbeutilizedtoenrichtheknowledgegraph. 3.KnowledgeRepresentation Oncetheknowledgeisacquired,itneedstoberepresentedinastructuredformthatcanbeeasilyprocessedbytheretrievalsystem.Knowledgegraphs,apowerfulrepresentationmodel,provideawaytorepresentknowledgeinagraphstructure,whereentitiesarerepresentedasnodesandrelationshipsbetweenentitiesarerepresented