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Date : 2014/09/18 Author : Niket Tandon, Gerard de Melo, Fabian Suchanek, Gerhard Weikum Source : WSDM’14 Advisor : Jia-ling Koh Speaker : Shao-Chun Peng

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Query-Performance prediction: Setting the Expectations Straight

Date : 2014/09/18Author : Niket Tandon , Gerard de Melo , Fabian Suchanek , Gerhard WeikumSource : WSDM14Advisor : Jia-ling KohSpeaker : Shao-Chun Peng1WebChild: Harvesting and Organizing Commonsense Knowledge from the WebOutline2IntroductionApproachExperimentalConclusion

Introduction3What is knowledge bases ?Web searchtext analyticsRecommendations in social media

Introduction4MotivationComputers completely lack this kind of commonsense knowledge

Round and red

Introduction5How about teach computer?

Round and red

Introduction6PurposeAutomatically extracting and cleaning commonsense properties from the Web

Introduction7salient characteristics and unique qualitiesFine-grained assertionsDisambiguated argumentsMinimal supervisionOutline8IntroductionApproachRange PopulationDomain PopulationComputing AssertionsExperimentalConclusion

Sub task9Range PopulationDomain PopulationComputing Assertions

Range Population10Candidate GatheringGraph ConstructionEdgeWeightingLabel Propagation (LP)hasTaste: {delicious, spicy, hot, sweet, etc...}Range Population11Candidate GatheringGoogle N-gram corpus(5-gram)checking for the presence of the word r , any of its synonyms40000 adj. for all relation

Range Population12Graph Construction3 kinds of edgeedges among wordsedges between two senses u-ai and w-aj edges between words and senses

Range Population13EdgeWeightingedges among words

edges between two senses u-ai and w-aj taxonomic relatedness within WordNet [17]edges between words and sensesthe sense frequencies as a basis for edge weights

glosses of their respective hyponyms and hypernyms13Range Population14Label Propagation (LP)

ABDCABDCRelaton:0.8Dummy:0.2Relaton:0.7Dummy:0.30.80.2Relaton:0.8Dummy:0.20.80.2Relaton:0.1Dummy:0.9Domain Population15Candidate Gathering*Graph ConstructionEdgeWeightingLabel Propagation (LP)Beef: hasTaste

sour-a2:the taste experience when vinegar or lemon juice is taken . . (vinegar, sour-a2) and (lemon juice, sour-a2)gloss for sour-a2 reads the taste experience when vinegar or lemon juice is taken . . . . We generatetwo assertions from this: (vinegar, sour-a2) and (lemon juice, sour-a2)15Computing Assertions

16Graph Construction*EdgeWeighting*Label Propagation (LP)

Outline17IntroductionApproachExperimentalConclusion

Range Population18

Domain Population19

Computing Assertions20

Outline21IntroductionApproachExperimentalConclusion