Using Linked Data to Mine RDF from Wikipedia’s Tables [POSTER]

175389e8c3ad885108fc33f8f05ba9bd?s=47 Emir Muñoz
February 25, 2014

Using Linked Data to Mine RDF from Wikipedia’s Tables [POSTER]

In 7th ACM Web Search and Data Mining Conference (WSDM 2014), New York City, New York, 24-28 February

175389e8c3ad885108fc33f8f05ba9bd?s=128

Emir Muñoz

February 25, 2014
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  1. ACKNOWLEDGEMENTS: This work was supported in part by Fujitsu (Ireland)

    Ltd., by the Millennium Nucleus Center for Semantic Web Research under Grant NC120004, and by Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289. Using Linked Data to Mine RDF from Wikipedia’s Tables http://emunoz.org/wikitables Emir Muñoz Fujitsu (Ireland) Limited, Galway Aidan Hogan DCC, Universidad de Chile Alessandra Mileo INSIGHT NUI Galway MOTIVATION player http://dbpedia.org/resource/David_de_Gea http://dbpedia.org/resource/Rafael_Pereira_da_Silva_(footballer_born_1990) http://dbpedia.org/resource/Patrice_Evra …. http://dbpedia.org/resource/Fabio_Pereira_da_Silva http://dbpedia.org/resource/Tom_Cleverley http://dbpedia.org/resource/Darren_Fletcher SPARQL QUERY: SELECT ?player WHERE { ?player dbp:currentclub dbr:Manchester_United_F.C } … INCOMPLETE RESULTS! PROPOSAL http://dbpedia.org/resource/Manchester_United_F.C. http://dbpedia.org/resource/England http://dbpedia.org/resource/Forward_(association_football) http://dbpedia.org/resource/Wayne_Rooney dbo:birthPlace dbp:position http://dbpedia.org/resource/Spain http://dbpedia.org/resource/Goalkeeper_(association_football) http://dbpedia.org/resource/David_de_Gea dbp:position http://dbpedia.org/resource/Brazil http://dbpedia.org/resource/Defender_(association_football) http://dbpedia.org/resource/Fabio_Pereira_da_Silva dbp:position dbp:currentclub … (1) dbr:David_de_Gea dbo:birthPlace dbr:Spain . (2) dbr:Fabio_Pereira_de_Silva dbo:birthPlace dbr:Brazil . (3) dbr:Fabio_Pereira_de_Silva dbp:currentclub dbr:Manchester_United_F.C . SUGGESTED TRIPLES: WIKITABLE SURVEY TABLE TAXONOMY: DISTRIBUTIONS: RESULTS (1) EXTRACTED 34.9 MILLION UNIQUE & NOVEL TRIPLES FROM 1.14 MILLION WIKITABLES (8 MACHINES: 4GB RAM, 2.2 GHZ SINGLE CORE; 12 DAYS) (2) INITIAL EVALUATION: (MANUAL ANNOTATION; THREE JUDGES; 750 TRIPLES EACH) (3) MACHINE LEARNING CLASSIFIERS: (CONSENSUS GOLD STANDARD; VARIETY OF FEATURES) FROM 1.14 MILLION WIKITABLES: BAGGING DECISION TREES: SUPPORT VECTOR MACHINES: 1.14 MILLION WIKITABLES: 7.9 MILLION TRIPLES 81.5% PREC., 78.1% ACC., 77.4% REC. 15.3 MILLION TRIPLES 72.4% PREC., 72.6%ACC., 75.8% REC. …