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City State Mayor Population Baltimore MD S.C.Rawlings- Blake 637,418 Seattle WA M.McGinn 617,334 Boston MA T.Menino 645,169 Raleigh NC C.Meeker 405,791 We are laying a strong foundation for the Semantic Web … but an old problem haunts us …. Chicken ? Egg ? Lack of structured data on the Semantic web More than a trillion documents on the web ~ 14.1 billion tables, 154 million with high quality relational data 305,632 Datasets available on Data.gov (US) + 7 Other nations establishing open data; not all is RDF Is it practical to convert this into RDF manually ? NO !! We need systems that can (semi) automatically convert and represent data for the Semantic Web ! Interpreting and Representing Tables Interpreting and Representing Tables as Linked Data as Linked Data http://dbpedia.org/ resource/Seattle http://dbpedia.org/ ontology/ AdministrativeRegion Map numbers to property- values dbpedia- prop:larges tCity 1 2 4 3 Predict Class Labels Link table cells Identify Relations T2LD framework We develop a multi- stage framework for this task City Baltimor e Seattle Boston Raleigh Instance Class Class for the column For every string in the column, query the knowledge base (KB) Generate a set of possible classes Rank the classes and choose the best Wikitol ogy Yago Wikitology (our KB) is a hybrid KB consisting of structured and unstructured data from Wikipedia, Dbpedia, Freebase, WordNet and Yago Linking table cells – A machine learning based approach Re-query KB with predicted class label as additional evidence An SVM-Rank classifier ranks the result set A second SVM classifier decides whether to link to the top-ranked instance or not City Baltimor e Seattle Boston Raleigh State MD WA MA NC Relation ‘A’ Relation ‘A’,’B’ Relation ‘A’, ‘C’ Relation ‘A’ Relation ‘A’ For every pair of linked strings in the two column, query the knowledge base (KB) Generate a set of possible relations Relation identification A template for representing tables as linked data @prefix rdfs: <http://www.w3.org/2000/01/rdf- schema#> . @prefix dbpedia: <http://dbpedia.org/resource/> . @prefix dbpedia-owl: <http://dbpedia.org/ontology/> . @prefix dbpprop: <http://dbpedia.org/property/> . "City"@en is rdfs:label of dbpedia-owl:City . "State"@en is rdfs:label of dbpedia- owl:AdminstrativeRegion . "Baltimore"@en is rdfs:label of dbpedia:Baltimore . dbpedia:Baltimore a dbpedia-owl:City . "MD"@en is rdfs:label of dbpedia:Maryland . dbpedia:Maryland a dbpedia- owl:AdministrativeRegion . dbpprop:LargestCity rdfs:domain dbpedia- owl:AdminstrativeRegion . dbpprop:LargestCity rdfs:range dbpedia-owl:City . MAP > 0 for 81 % of the columns Recall >= 0.6 for 75 % of the columns Column – Nationality Column – Birth Place Prediction – MilitaryConflict Prediction – PopulatedPlace Column Correctness Entity Linking Preliminary results for relation identification – 25 % accuracy Relation Identification # of Tables 15 # of Columns 52 # of Entities 611 Dataset Varish Mulwad, Tim Finin, Zareen Syed Varish Mulwad, Tim Finin, Zareen Syed and Anupam Joshi and Anupam Joshi University of Maryland, Baltimore County University of Maryland, Baltimore County

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Page 1: CityStateMayorPopulation BaltimoreMDS.C.Rawlings-Blake637,418 SeattleWAM.McGinn617,334 BostonMAT.Menino645,169 RaleighNCC.Meeker405,791 We are laying a

City State Mayor PopulationBaltimore MD S.C.Rawlings-Blake 637,418

Seattle WA M.McGinn 617,334

Boston MA T.Menino 645,169

Raleigh NC C.Meeker 405,791

We are laying a strong foundation for the Semantic Web

… but an old problem haunts us …. Chicken ? Egg ?

Lack of structured data on the Semantic web

More than a trillion documents on the web ~ 14.1 billion tables, 154 million with high

quality relational data 305,632 Datasets available on Data.gov (US)

+ 7 Other nations establishing open data; not all is RDF

Is it practical to convert this into RDF manually ? NO !!

We need systems that can (semi) automatically convert

and represent data for the Semantic Web !

We need systems that can (semi) automatically convert

and represent data for the Semantic Web !

Interpreting and Representing Tables as Linked DataInterpreting and Representing Tables as Linked Data

http://dbpedia.org/resource/Seattlehttp://dbpedia.org/resource/Seattle

http://dbpedia.org/ontology/

AdministrativeRegion

http://dbpedia.org/ontology/

AdministrativeRegion

Map numbers to property-valuesMap numbers to property-values

dbpedia-prop:largestCity

dbpedia-prop:largestCity

1 2

43

Predict Class Labels

Predict Class Labels Link table cellsLink table cells Identify RelationsIdentify Relations

T2LD framework

We develop a multi-stage framework for this task

CityBaltimoreSeattleBostonRaleigh

Instance

Class

Class for the column

For every string in the column, query the knowledge base (KB)

Generate a set of possible classes

Rank the classes and choose the best

Wikitology

Yago

Wikitology (our KB) is a hybrid KB consisting of structured and unstructured data from Wikipedia, Dbpedia, Freebase, WordNet and Yago

Linking table cells – A machine learning based approach

Re-query KB with predicted class label as additional

evidence

Re-query KB with predicted class label as additional

evidence

An SVM-Rank classifier ranks the result set

An SVM-Rank classifier ranks the result set

A second SVM classifier decides whether to link to the top-ranked instance or

not

A second SVM classifier decides whether to link to the top-ranked instance or

not

CityBaltimoreSeattleBostonRaleigh

StateMDWAMANC

Relation ‘A’Relation ‘A’

Relation ‘A’,’B’Relation ‘A’,’B’

Relation ‘A’, ‘C’Relation ‘A’, ‘C’

Relation ‘A’Relation ‘A’

Relation ‘A’Relation ‘A’

For every pair of linked strings in the two column, query the knowledge base (KB)

Generate a set of possible relations Rank the relations and choose the relation

Relation identification

A template for representing tables as linked data

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .@prefix dbpedia: <http://dbpedia.org/resource/> .@prefix dbpedia-owl: <http://dbpedia.org/ontology/> .@prefix dbpprop: <http://dbpedia.org/property/> ."City"@en is rdfs:label of dbpedia-owl:City ."State"@en is rdfs:label of dbpedia-owl:AdminstrativeRegion ."Baltimore"@en is rdfs:label of dbpedia:Baltimore .dbpedia:Baltimore a dbpedia-owl:City ."MD"@en is rdfs:label of dbpedia:Maryland .dbpedia:Maryland a dbpedia-owl:AdministrativeRegion .dbpprop:LargestCity rdfs:domain dbpedia-owl:AdminstrativeRegion .dbpprop:LargestCity rdfs:range dbpedia-owl:City .

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .@prefix dbpedia: <http://dbpedia.org/resource/> .@prefix dbpedia-owl: <http://dbpedia.org/ontology/> .@prefix dbpprop: <http://dbpedia.org/property/> ."City"@en is rdfs:label of dbpedia-owl:City ."State"@en is rdfs:label of dbpedia-owl:AdminstrativeRegion ."Baltimore"@en is rdfs:label of dbpedia:Baltimore .dbpedia:Baltimore a dbpedia-owl:City ."MD"@en is rdfs:label of dbpedia:Maryland .dbpedia:Maryland a dbpedia-owl:AdministrativeRegion .dbpprop:LargestCity rdfs:domain dbpedia-owl:AdminstrativeRegion .dbpprop:LargestCity rdfs:range dbpedia-owl:City .

MAP > 0 for81 % ofthe columns

Recall >= 0.6 for 75 % of the columns

Column – Nationality Column – Birth PlacePrediction – MilitaryConflict Prediction – PopulatedPlaceColumn – Nationality Column – Birth PlacePrediction – MilitaryConflict Prediction – PopulatedPlace

Column Correctness

Entity Linking

Preliminary results for relation identification – 25 % accuracy Preliminary results for relation identification – 25 % accuracy

Relation Identification

# of Tables 15# of Columns 52# of Entities 611

Dataset

Varish Mulwad, Tim Finin, Zareen Syed and Anupam JoshiVarish Mulwad, Tim Finin, Zareen Syed and Anupam JoshiUniversity of Maryland, Baltimore CountyUniversity of Maryland, Baltimore County

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