Semantic annotation of Web data applied to risk in food.
A preliminary step to risk in food assessment is the gathering of experimental data. In the framework of the Sym'Previus project (http://www.symprevius.org), a complete data integration system has been designed, grouping data provided by industrial partners and data extracted from papers published in the main scientific journals of the domain. Those data have been classified by means of a predefined vocabulary, called ontology. Our aim is to complement the database with data extracted from the Web. In the framework of the WebContent project (www.webcontent.fr), we have designed a semi-automatic acquisition tool, called @WEB, which retrieves scientific documents from the Web. During the @WEB process, data tables are extracted from the documents and then annotated with the ontology. We focus on the data tables as they contain, in general, a synthesis of data published in the documents. In this paper, we explain how the columns of the data tables are automatically annotated with data types of the ontology and how the relations represented by the table are recognised. We also give the results of our experimentation to assess the quality of such an annotation.
Hignette
Gaëlle
G
INRA MIA, Unité Mét@risk UR 1204, 16, rue Claude Bernard 75231 Paris Cédex 5, France; AgroParisTech, UFR Informatique, 16, rue Claude Bernard, 75 231 Paris Cedex 05, France. gaelle.hignette@gmail.com
Buche
Patrice
P
Couvert
Olivier
O
Dibie-Barthélemy
Juliette
J
Doussot
David
D
Haemmerlé
Ollivier
O
Mettler
Eric
E
Soler
Lydie
L
D001419:Bacteria / Q000254:growth & development*; D015169:Colony Count, Microbial; D005506:Food Contamination / Q000032:analysis*; D005516:Food Microbiology; D006801:Humans; D020407:Internet; D007700:Kinetics; D008433:Mathematics; D008954:Models, Biological; D011237:Predictive Value of Tests; D018570:Risk Assessment / Q000379:methods*