=Paper=
{{Paper
|id=Vol-2807/abstractS
|storemode=property
|title=A New Alignment Method Based on FoodOn as Pivot Ontology to Manage Incompleteness in Nutritional Legacy Data Sources (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2807/abstractS.pdf
|volume=Vol-2807
|authors=Patrice Buche,Cufi Julien,Stéphane Dervaux,Juliette Dibie,Liliana Ibanescu,Alrick Oudot,Magalie Weber
|dblpUrl=https://dblp.org/rec/conf/icbo/BucheJDDIOW20
}}
==A New Alignment Method Based on FoodOn as Pivot Ontology to Manage Incompleteness in Nutritional Legacy Data Sources (short paper)==
https://ceur-ws.org/Vol-2807/abstractS.pdf
A new alignment method based on FoodOn
as pivot ontology to manage
incompleteness in nutritional legacy data
sources
Patrice BUCHEa, b,1, Julien CUFI b , Stéphane DERVAUX c , Juliette DIBIE c , Liliana
IBANESCU c , Alrick OUDOT b and Magalie WEBER d
a
LIRMM, Univ Montpellier, CNRS, INRIA GraphIK, Montpellier, France
b
IATE, Univ Montpellier, INRA, CIRAD, Montpellier SupAgro, Montpellier, France
c
UMR MIA-Paris, AgroParisTech, INRA, University Paris-Saclay, Paris, France
d
BIA INRA, Nantes, France
Abstract. In order to correctly assess the nutritional quality of a meal or a
manufactured food product in a given country, the first step is to assess the
nutritional values for its ingredients. Food composition databases (FCDBs)
available in a lot of countries and managed at national level provide values for
energy and nutrients of food components. Unfortunately, values associated with
some nutrients of interest may be lacking in the FCDB of the country in which the
nutritional quality must be assessed. Finding values associated with nutrients for
similar foods in other FCDBs is a way to deal with incompleteness. An additional
issue arises because the vocabulary used to describe the ingredients of a meal or a
recipe in a given FCDB is usually different from the one used in other ones. In this
paper we address the problem of identifying the nutritional value of a recipe's
ingredients by querying different FCDBs through FoodOn as pivot ontology. We
present a new alignment method between two distinct FCDBs, based on syntactic
and semantic approaches, whose vocabulary is previously transformed into an
ontology. Our method has been evaluated on Ciqual, the French food nutritional
database and USDA, the United States food nutritional. The incompleteness
management task based on FoodOn as pivot ontology has been assessed with a real
use-case concerning iron, Vitamin B12, Vitamin C nutrients.
Keywords. Ontology alignment, Food composition databases, FoodOn, LanguaL
In the framework of the French Meatylab project including industrial partners, we were
asked to propose a solution to combine data retrieved from several Food composition
databases (FCDB) [1] managed by different agencies and countries to assess the
nutritional values of a recipe. Each agency has its own way of describing a food product,
whether in terms of labeling or categorization in different facets. Unfortunately, values
associated with some nutrients of interest may be lacking in the FCDB of the country in
which the nutritional quality must be assessed. Finding values associated with nutrients
for similar foods in other FCDBs is a way to deal with incompleteness. Consequently,
our goal is to offer a multi-base query tool based on an ontology to establish links
1
Corresponding Author
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
between similar food concepts. This functionality is not available in state of the art tools
(eg. EuroFIR FoodExplorer).
As an example, this tool should be able to use a term (for example example
‘Courgette, puree’ from CIQUAL FCDB), to be able to recover all the products and
associated nutritional values (e.g. ‘Squash, winter, acorn, cooked, boiled, mashed,
without salt’ in USDA FCDB). To achieve this, we use as background knowledge, the
LanguaL description [2] associated with the food term defined in each national agency.
LanguaL stands for "Langua aLimentaria" or "language of food". These descriptions
provide a multi-facets semantic definition of a given food expressed in a standardized
vocabulary that we will use to find similarities between food products belonging to the
vocabulary of different agencies. More than 40.000 foods used in food composition
databases are LanguaL described [5].
Our method also takes into account English labels associated with food products in
FCDBs. The pivot of all these vocabularies is FoodOn [3], an ontology dedicated to food
description. FoodOn is a food ontology initially based on a conversion of the LanguaL
thesaurus. For instance, each specialization terms' hierarchy associated with each
LanguaL facet was tranlasted in FoodOn into a specialization concepts' hierarchy.
Additionally, FoodOn includes 9.500 food terms imported from the Scientific
Information and Retrieval Exchange Network of the US Food and Drug administration
food database that are organized in families and described in LanguaL.
Our approach aligns the food products of the different FCDBs on FoodOn, based on
LanguaL faceted descriptions (semantic approach) in addition to product labels
(syntactic approach). This combination of both approaches permits to overcome both the
lack of faceted description for some products and the gaps in a purely syntactic
comparison (the same food may be denoted differently in different FCDBs).
The main originality of our alignment approach is to reuse Langual descriptions
associated with FCDBs food terms available on Langual website combining relevant
alignment methods already known in the state of the art [4]. We will present main
principles of the approach and results obtained to deal with the lack of values associated
with Vitamin C, Vitamin B12 and iron for a set of Ciqual food products reusing values
associated with similar foods in USDA.
References
[1] Pehrsson, P. and Haytowitz, D. (2016). Food composition databases. In Caballero, B., Finglas, P. M., and
Toldra_, F., editors, Encyclopedia of Food and Health, pages 16-21. Academic Press, Oxford.
[2] Ireland, J. and Moller, A. (2016). Food classification and description. In Caballero, B., Finglas, P. M., and
Toldra_, F., editors, Encyclopedia of Food and Health, pages 1-6. Academic Press, Oxford.
[3] Dooley, D. M., Griffiths, E. J., Gosal, G., Buttigieg, P. L., Hoehndorf, R., Lange, M., Schriml, L. M.,
Brinkman, F. S. L., and Hsiao,W.W. L. (2018). Foodon: a harmonized food ontology to increase global
food traceability, quality control and data integration. In npj Science of Food.
[4] Shvaiko, P. and Euzenat, J. (2013). Ontology matching: State of the art and future challenges. IEEE
Transactions on Knowledge and Data Engineering, 25:158-176.
[5] LanguaL indexed Datasets (2020) The LanguaL indexed Datasets.
http://langual.org/langual_indexed_datasets.asp