scholarly journals A Framework for Quality Assessment of Semantic Annotations of Tabular Data

2021 ◽  
pp. 528-545
Author(s):  
Roberto Avogadro ◽  
Marco Cremaschi ◽  
Ernesto Jiménez-Ruiz ◽  
Anisa Rula

Author(s):  
Marco Cremaschi ◽  
Anisa Rula ◽  
Alessandra Siano ◽  
Flavio De Paoli


2019 ◽  
Vol 181 ◽  
pp. 104824 ◽  
Author(s):  
Roberto Álvarez Sánchez ◽  
Andoni Beristain Iraola ◽  
Gorka Epelde Unanue ◽  
Paul Carlin


Author(s):  
Jiaoyan Chen ◽  
Ernesto Jimenez-Ruiz ◽  
Ian Horrocks ◽  
Charles Sutton

The usefulness of tabular data such as web tables critically depends on understanding their semantics. This study focuses on column type prediction for tables without any meta data. Unlike traditional lexical matching-based methods, we propose a deep prediction model that can fully exploit a table’s contextual semantics, including table locality features learned by a Hybrid NeuralNetwork (HNN), and inter-column semantics features learned by a knowledge base (KB) lookup and query answering algorithm. It exhibits good performance not only on individual table sets, but also when transferring from one table set to another.





1997 ◽  
Vol 24 (7) ◽  
pp. 496-505 ◽  
Author(s):  
E. S. GROSSMAN ◽  
J. M. MATEJKA
Keyword(s):  


2002 ◽  
Vol 12 (2) ◽  
pp. 135-145 ◽  
Author(s):  
B.S. Yilbas ◽  
M. Rashid


PsycCRITIQUES ◽  
2006 ◽  
Vol 51 (14) ◽  
Author(s):  
Howard N. Garb
Keyword(s):  


2018 ◽  
Author(s):  
Artur Jaschke ◽  
Laura H. P. Eggermont ◽  
Sylka Uhlig ◽  
Erik J. A. Scherder




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