Triplifying Wikipedia's tables

Emir Muñoz; Aidan Hogan; Alessandra Mileo

doi:10.13025/21361

Triplifying Wikipedia's tables

Emir Muñoz
, Aidan Hogan
, Alessandra Mileo

Research output: Contribution to conference (Published) › Paper

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Abstract

We are currently investigating methods to triplify the content of Wikipedia's tables. We propose that existing knowledge-bases can be leveraged to semi-automatically extract high-quality facts (in the form of RDF triples) from tables embedded in Wikipedia articles (henceforth called \Wikitables"). We present a survey of Wikitables and their content in a recent dump of Wikipedia. We then discuss some ongoing work on using DBpedia to mine novel RDF triples from these tables: we present methods that automatically extract 24.4 million raw triples from the Wikitables at an estimated precision of 52.2%. We believe this precision can be (greatly) improved through machine learning methods and sketch ideas for features that should help classify (in)correct triples.

Original language	English (Ireland)
DOIs	https://doi.org/10.13025/21361
Publication status	Published - 1 Jan 2013
Externally published	Yes

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10.13025/21361Licence: CC BY-NC-ND

LD4IE 2013 wikitablesAccepted author manuscript, 757 KBLicence: CC BY-NC-ND

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abstract = "We are currently investigating methods to triplify the content of Wikipedia's tables. We propose that existing knowledge-bases can be leveraged to semi-automatically extract high-quality facts (in the form of RDF triples) from tables embedded in Wikipedia articles (henceforth called \textbackslash{}Wikitables{"}). We present a survey of Wikitables and their content in a recent dump of Wikipedia. We then discuss some ongoing work on using DBpedia to mine novel RDF triples from these tables: we present methods that automatically extract 24.4 million raw triples from the Wikitables at an estimated precision of 52.2\%. We believe this precision can be (greatly) improved through machine learning methods and sketch ideas for features that should help classify (in)correct triples.",

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AU - Hogan, Aidan

AU - Mileo, Alessandra

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N2 - We are currently investigating methods to triplify the content of Wikipedia's tables. We propose that existing knowledge-bases can be leveraged to semi-automatically extract high-quality facts (in the form of RDF triples) from tables embedded in Wikipedia articles (henceforth called \Wikitables"). We present a survey of Wikitables and their content in a recent dump of Wikipedia. We then discuss some ongoing work on using DBpedia to mine novel RDF triples from these tables: we present methods that automatically extract 24.4 million raw triples from the Wikitables at an estimated precision of 52.2%. We believe this precision can be (greatly) improved through machine learning methods and sketch ideas for features that should help classify (in)correct triples.

AB - We are currently investigating methods to triplify the content of Wikipedia's tables. We propose that existing knowledge-bases can be leveraged to semi-automatically extract high-quality facts (in the form of RDF triples) from tables embedded in Wikipedia articles (henceforth called \Wikitables"). We present a survey of Wikitables and their content in a recent dump of Wikipedia. We then discuss some ongoing work on using DBpedia to mine novel RDF triples from these tables: we present methods that automatically extract 24.4 million raw triples from the Wikitables at an estimated precision of 52.2%. We believe this precision can be (greatly) improved through machine learning methods and sketch ideas for features that should help classify (in)correct triples.

UR - http://hdl.handle.net/10379/6019

U2 - 10.13025/21361

DO - 10.13025/21361

M3 - Paper

ER -

Triplifying Wikipedia's tables

Abstract

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