chapter

A domain independent framework for extracting linked semantic data from tables

Authors:

Anupam JoshiAuthors Info & Claims

Search Computing: broadening web search

January 2012

Pages 16 - 33

Published: 01 January 2012 Publication History

Abstract

Vast amounts of information is encoded in tables found in documents, on the Web, and in spreadsheets or databases. Integrating or searching over this information benefits from understanding its intended meaning and making it explicit in a semantic representation language like RDF. Most current approaches to generating Semantic Web representations from tables requires human input to create schemas and often results in graphs that do not follow best practices for linked data. Evidence for a table's meaning can be found in its column headers, cell values, implicit relations between columns, caption and surrounding text but also requires general and domain-specific background knowledge. Approaches that work well for one domain, may not necessarily work well for others. We describe a domain independent framework for interpreting the intended meaning of tables and representing it as Linked Data. At the core of the framework are techniques grounded in graphical models and probabilistic reasoning to infer meaning associated with a table. Using background knowledge from resources in the Linked Open Data cloud, we jointly infer the semantics of column headers, table cell values (e.g., strings and numbers) and relations between columns and represent the inferred meaning as graph of RDF triples. A table's meaning is thus captured by mapping columns to classes in an appropriate ontology, linking cell values to literal constants, implied measurements, or entities in the linked data cloud (existing or new) and discovering or and identifying relations between columns.

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Cited By

de Vos MWielemaker JRijgersberg HSchreiber GWielinga BTop J(2017)Combining information on structure and content to automatically annotate natural science spreadsheetsInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2017.02.006103:C(63-76)Online publication date: 1-Jul-2017
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cover image Guide books

Search Computing: broadening web search

January 2012

255 pages

ISBN:9783642342127

Editors:
Stefano Ceri
Dipartimento di Elettronica e Informazione,, Politecnico di Milano, Via Ponzio, 34/5, Milan, Italy
,
Marco Brambilla
Dipartimento di Elettronica e Informazione, Politecnico di Milano, Via Ponzio, 34/5, Milan, Italy

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2012

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Cited By

de Vos MWielemaker JRijgersberg HSchreiber GWielinga BTop J(2017)Combining information on structure and content to automatically annotate natural science spreadsheetsInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2017.02.006103:C(63-76)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.ijhcs.2017.02.006
Compton MRatcliffe DShu YSquire GDesai BToyama M(2015)Declarative Data ExchangeProceedings of the 19th International Database Engineering & Applications Symposium10.1145/2790755.2790764(96-105)Online publication date: 13-Jul-2015
https://dl.acm.org/doi/10.1145/2790755.2790764
Cruz IGanesh VMirrezaei S(2013)Semantic extraction of geographic data from web tables for big data integrationProceedings of the 7th Workshop on Geographic Information Retrieval10.1145/2533888.2533939(19-26)Online publication date: 5-Nov-2013
https://dl.acm.org/doi/10.1145/2533888.2533939
Taheriyan MKnoblock CSzekely PAmbite J(2013)A Graph-Based Approach to Learn Semantic Descriptions of Data SourcesProceedings of the 12th International Semantic Web Conference - Part I10.1007/978-3-642-41335-3_38(607-623)Online publication date: 21-Oct-2013
https://dl.acm.org/doi/10.1007/978-3-642-41335-3_38
Mulwad VFinin TJoshi A(2013)Semantic Message Passing for Generating Linked Data from TablesProceedings of the 12th International Semantic Web Conference - Part I10.1007/978-3-642-41335-3_23(363-378)Online publication date: 21-Oct-2013
https://dl.acm.org/doi/10.1007/978-3-642-41335-3_23

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