HyKSS: Hybrid Keyword and Semantic SearchAndrew Zitzelberger*

Keyword Search*

Form Based Search*

*over 8,000 meters in elevationless than 100K milesfaster than 100 mphWhat about?

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HyKSSHybrid Keyword and Semantic SearchSemantics extracted annotationsMultiple ontologiesKeywords text*

Thesis StatementHyKSS (hybrid search)Outperforms keyword and semantic searchDynamic query weighting outperforms various other hybrid search approachesAllows queries over multiple ontologiesAllows pay-as-you-go improvement*

Extraction Ontologies*

Data Frames*

Indexing Architecture*Keyword IndexerSemantic IndexerKeyword IndexSemantic IndexDocument Collection

Indexing Architecture Implementation**OntoESOntologyLibrarySesameLucene

Query Processing*Free Form QueryExecute QueryPost-Process QueryCombine ResultsPre-Process QueryExecute QueryPost-Process QueryPre-Process QueryKeyword ProcessingSemantic Processing

Keyword Query Pre-Processing*Remove Lucene special characters (except quotes)Remove (inequality) comparison constraintsRemove non-phrase stopwords

hondas in "excellent condition" in orem for under 12 grand

hondas excellent condition orem

Keyword Query Execution and Post-ProcessingExecuted by LuceneEmpty Post-Processing step*

Semantic Query Pre-Processing Individual Ontology Scoring hondas in "excellent condition" in orem for under 12 grand

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Semantic Query Pre-Processing Ontology Set CreationFor each ontology sorted by score:For each remaining ontology:Add point for each new or subsuming matchIf added points > 0 add ontologyCompletely subsumed ontologies are removed during query generation*

Semantic Query Pre-Processing Ontology Set Creation*Price < 12000LocationVehicleContractualServicesLocationVehicleContractualServicesVehicle_Score + 1US_City=oremPrice < 12000Price < 12000ContractualServices_Score + 1Vehicle_ScoreUS_City=orem

Semantic Query Pre-Processing Structured Query GenerationOpen world assumptionSPARQL query*

Semantic Query Execution and Post-ProcessingSesame query executionSemantic ranking:1 point for each requested projection satisfiedNormalized by # of projections requested

hondas in "excellent condition" in orem for under 12 grandProjections on Make, Price and US_City

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Hybrid Query ProcessingLinear interpolation:(kw_weight * kw_score) + (sm_weight * sm_score)Dynamic solution:# keywords remaining (#kw)concept match score (cms) = * (selections + projections)kw_weight = #kw/(#kw + cms)sm_weight = cms/(#kw + cms)

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Basic Search*

Results Display*

*Form Based Search

Results Display

Experimental Setup Ontology Libraries5 Ontology LevelsNumberGeneric UnitsVehicle UnitsVehicleVehicle+*

Experimental Setup Query Sets113 syntactically unique queries from database students60 syntactically unique queries from linguistic students*

Experimental Setup Document Collection250 vehicle advertisements (Craigslist)100 training, 50 validation, 100 test318 mountain pages (Wikipedia)66 roller coaster (Wikipedia)88 video game advertisements (Craigslist)

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ExperimentsTraining queries over test vehicle documentsTest queries over test vehicle documentsTraining queries over test vehicle documents + additional noiseTest queries over test vehicle documents + additional noise5 queries over noisy data (Generic Units only)*

Experiments - MetricMean Average Precision

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Experimental Results*

Experimental Results*

Experimental Results*

ConclusionsHybrid search outperforms keyword and semantic searchHyKSSs dynamic query weighting approach outperforms various other weighting techniquesUsing multiple does not outperform selecting and using a single ontology*

External Image CitationsSlide 2 Google search screenshot: http://www.google.com (07/30/11)Slide 3 partial car search form screenshots: http://autotrader.com/fyc (07/30/11)Slide 4 mountain image: http://en.wikipedia.org/wiki/Lhotse (04/26/11)Slide 4 car image: http://en.wikipedia.org/wiki/Honda (04/26/11)Slide 4 roller coaster image: http://en.wikipedia.org/wiki/Kingda_Ka (04/26/11)Slide 4 Wikipedia logo: http://en.wikipedia.org/wiki/Main_Page (04/26/11)Slide 4 craigslist logo: http://provo.craigslist.org/ (04/26/11)

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