中文知识图谱构建的关键技术.pdf

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1、IntelligentSearchEngineandRecommenderSystemsbasedonKnowledgeGraph阳德青复旦大学知识工场实验室yangdeqing@fudan.edu.cn2017-07-13Background•KnowledgeGraphexhibitsitsexcellentperformancethroughtheintelligentapplicationsbuiltonit•AstypicalAIsystems,Searchengineandrecommendersystemareverypopularandpromisin

2、gintheeraoflargedata•ManypreviousliteraturesandsystemshaveprovedKG’smeritsonsuchAI’sapplicationsKG-basedSearchEngineTheHistoryofSearchEngine•1st:category-based•Thekeywordofhighclick•Yahoo,hao123frequencyarerankedhigher•2nd:IR-based•Thepagescontainingthekeywordsofmoreweights•Keyword-base

3、d,vectorresultinarerankedhigherspace,Booleanmodel•Thepageshavingmore•3rd:link-basedimportantin-linksareranked•PageRank(Google)higherHowever,howtohandleitifuserswanttosearchsomethingnewortheonesoflongtail?TheHistoryofSearchEngine•WhenKnowledgeGraphemergesthing/entitykeyword/stringrelevan

4、tentitiesTheHistoryofSearchEngine•Search4.0:user-basedsearchengine•Reallyunderstandthesearchintentofusers,especiallyforthoserareentities•Directlyreturnpureanswers(entities)insteadofrelevantwebpages•Provideplentiful/relevantresultswithhighconfidence/interpretabilityintelligentsearchengin

5、eItisactuallyarecommendation.KnowledgegraphcanhelptheengineachievethesegoalsEntitySuggestionwithConceptualExplanation[1]•MorethansearchiPhone6PlusorCananysearchenginereturnsuchresults?•Problemstatement•Givenaqueryqdescribedbyasetofentities,theengineshouldreturnsomerelevantentitiesalongw

6、ithsomefine-grainedconceptswhichcanwellexplaintheresultsEntitySuggestionwithConceptualExplanation•Whataretherelevantentitiesandfine-grainedconcepts?GrowingMarketEmergingeconomyBRICCountryWhatentitiesshouldbesuggested?CompanyChinesecompanyChineseinternetgiantEntitySuggestionwithConceptua

7、lExplanation•PreviousWorks•1.Co-occurrence(textorquerylog),e.g.GoogleSet,BayesianSet•2.Weblists,e.g.SEAL,SEISA•3.Overlapofproperties•Weakness•1.Co-occurrence≠conceptualcoherence•2.Membershipoflists→toogeneral/specificconceptsorarandomlycombinedlist•3.Overlapofproperties≠concept