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{{Paper
|id=Vol-2807/abstractW
|storemode=property
|title=An Insight into Food Semantics: Review, Analysis, and Lessons Learnt over Food-Related Studies (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2807/abstractW.pdf
|volume=Vol-2807
|authors=Gorjan Popovski,Gordana Ispirova,Eva Valenčič,Riste Stojanov,Tome Eftimov,Barbara Koroušić Seljak
|dblpUrl=https://dblp.org/rec/conf/icbo/PopovskiIVSEK20
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==An Insight into Food Semantics: Review, Analysis, and Lessons Learnt over Food-Related Studies (short paper)==
https://ceur-ws.org/Vol-2807/abstractW.pdf
July 2020
An Insight into Food Semantics: Review,
Analysis, and Lessons Learnt over
Food-related Studies
Gorjan Popovski a,b , Gordana Ispirova a,b , Eva Valenčič a,b , Riste Stojanov c ,
Tome Eftimov a , Barbara Koroušić Seljak a,1
a Computer Systems Department, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
b Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
c Faculty of Computer Science and Engineering, Ss. Cyril and Methodius, University,
1000 Skopje, North Macedonia
A great amount of work has been done in predictive modelling in the past decades.
This is made possible by the existence of biomedical vocabularies and standards. De-
spite the availability of such resources in the health domain, the domain of food and
nutrition is low-resourced and can greatly benefit from such methods. Lancet Planetary
Health published that starting from 2019 the focus will be on the links between food
systems, human health, and the environment. Several food ontologies exist, but each de-
veloped for a specific application scenario. Hence, in 2019, the Big Food and Nutrition
Data Management and Analysis (BFNDMA) workshop started at the IEEE International
Conference on Big Data 2 , focusing focuses on methodologies for big data management
and analysis for food and nutrition data.
Recently, in an effort to tackle the task of Information Extraction (IE) from unstruc-
tured text, several methodologies were proposed. DrNer [1] is a rule-based NER method
for extracting information from evidence-based dietary recommendations. The authors
have extended the methodology by creating a novel food NER method named FoodIE [2],
which solely focuses on extracting food entities. It incorporates computational linguistic
rules and semantic information. The authors have compared it to other existing food NER
methods, showing that FoodIE provides the most promising results [3]. The collection
of such information from different data sources is represented in various unstandardized
ways, leading to the task of data normalization. It is a crucial task to facilitate and enable
further analyses. Hence, StandFood [4] has been introduced - a semi-automatic system
for classifying and describing foods according to FoodEx2. It is based on lexical simi-
larity between food names. In a recent paper [5], the authors have conducted a domain-
coverage analysis of an existing language for describing foods (LanguaL) by using Rep-
resentation Learning (RL) methods, finding that the coverage of the food domain does
not link the concepts together well, accenting the need for future efforts in food data nor-
malization. FoodBase [6] is the first data corpus consisting of recipes (total of 23,000)
annotated with the food entities found in them. The authors extend this work by propos-
1 Corresponding E-mail: barbara.korousic@ijs.si.
2 http://cs.ijs.si/bfndma/BFNDMA.html
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
July 2020
ing a food data normalization method (FoodOntoMap [7]) that performs food concept
mapping across different food semantic resources. It is based on the use of food NER
methods to perform the mapping. In addition, a visualization tool (FoodViz [8]) provides
a framework aimed at making the links between different food standards understandable
by food subject-matter experts.
Finally, the authors in a recent paper [9] have performed a non-English language (i.e.
Slovenian) case study where they employ RL methods in order to address the semantic
similarity between food products’ names.
Addressing open gaps, the extracted food entities can further be linked with entities
from other domains (e.g. health, biomedicine, consumer and social sciences). This can
help in reducing knowledge gaps that inhibit public health goals as well as the optimal
development of scientific, agricultural and industrial policies, which requires relevant
information from all food science domains, such as food safety, food authenticity and
traceability, food sustainability, etc. Coupled with Representation Learning techniques,
this can pave the way for methods to extract information in order to improve personalized
nutrition and medicine, as well as public health.
Acknowledgments. This research was supported by the Slovenian Research Agency (research
core grant number P2-0098), and the European Union’s Horizon 2020 research and innovation
programme (FNS-Cloud, Food Nutrition Security) (grant agreement 863059). The information and
the views set out in this publication are those of the authors and do not necessarily reflect the
official opinion of the European Union. Neither the European Union institutions and bodies nor
any person acting on their behalf may be held responsible for the use that may be made of the
information contained herein.
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