=Paper=
{{Paper
|id=Vol-2969/paper11-IFOW
|storemode=property
|title=Refinement of the COHESIVE Information System towards
a Unified Ontology of Food Terms for the Public Health Organizations
|pdfUrl=https://ceur-ws.org/Vol-2969/paper11-IFOW.pdf
|volume=Vol-2969
|authors=Iolanda Mangone,Nicolas Radomski,Adriano Di Pasquale,Andrea Santurbano,Paolo Calistri,Cesare Cammà,Kitty Maassen
|dblpUrl=https://dblp.org/rec/conf/jowo/MangoneRPSCCM21
}}
==Refinement of the COHESIVE Information System towards
a Unified Ontology of Food Terms for the Public Health Organizations==
https://ceur-ws.org/Vol-2969/paper11-IFOW.pdf
Refinement of the COHESIVE Information System towards a
Unified Ontology of Food Terms for the Public Health
Organizations
Iolanda Mangone 1, Nicolas Radomski 1*, Adriano Di Pasquale 1, Andrea Santurbano 2, Paolo
Calistri 1, Cesare Cammà 1and Kitty Maassen 3
1
National Reference Centre (NRC) for Whole Genome Sequencing of microbial pathogens: data-base and
bioinformatics analysis (GENPAT), Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "Giuseppe
Caporale" (IZSAM), via Campo Boario, Teramo, 64100 (TE), Italy (www.izs.it)
2
LARUS Business Automation, via Bruno Maderna 7, Mestre, – 30174 (VE), Italy (www.larus-ba.it)
3
National Institute for Public Health and the Environment (RIVM), P.O. Box 1, Bilthoven, 3720 (BA), The
Netherlands (www.rivm.nl)
*
corresponding author
Abstract
Background. The task 4.1 of the One Health European joint programme (OHEJP) “One
Health Structure In Europe” (COHESIVE) focuses on integrating pathogen information from
public health, animal health and food safety surveillance at Member State level. Considered
information are metadata associated to each sample (i.e. isolation date, origin, matrix) and
whole genome sequencing (WGS) data from official laboratories (e.g. next generation
sequencing data and bioinformatics-based analytical outcomes).
Methods. A WEB-based platform called the COHESIVE Information System (CIS) has been
created with separate instances for three Member States, in order to provide a proof of concept
showing the advantages for surveillance and investigation of outbreaks at the genomic scale,
considering food as a source of human pathogens. Currently, a CIS Version 2 (CISv2) is
under development to integrate a unified food ontology at Member State level, taking into
account as a first step organizations from Italy, Norway and The Netherlands: countries
involved in the feasibility study foreseen in the project. More precisely, the last developments
focused on the harmonization of the foodborn disease biosample contextual data collected
over the past few decades (i.e. contextual metadata of foodborne samples sent in by labs for
sequencing) based on the rule-based text mining tool LexMapr, and the implementation of the
FoodOn ontology into the CIS based on the graph-database Neo4j to allow future records of
harmonized food terms in the CISv2.
Results. The successful harmonization of the past food terms and implementation of the
FoodOn ontology into the CIS were mandatory steps allowing food ontology harmonization
between organizations and improvement of queries from the CISv2 based on relational- and
graph-databases.
Keywords 1
COHESIVE information system, food ontology, relational-database, graph-database,
genomics-based surveillance
1
IFOW 2021: 2nd Integrated Food Ontology Workshop, held at JOWO 2021: Episode VII The Bolzano Summer of Knowledge, September
11-18, 2021, Bolzano, Italy
EMAIL: i.mangone@izs.it (I. Mangone); n.radomski@izs.it (N. Radomski); a.dipasquale@izs.it (A. Dipasquale); andrea.santurbano@larus-
ba.it (A. Santurbano); p.calistri@izs.it (P. Calistri); c.camma@izs.it (C. Camma); kitty.maassen@rivm.nl (K. Maassen)
ORCID: https://orcid.org/0000-0002-6716-091X (I. Mangone); https://orcid.org/0000-0002-7480-4197 (N. Radomski);
https://orcid.org/0000-0002-9328-3972 (A. Dipasquale); https://orcid.org/0000-0001-5066-8971 (A. Santurbano); https://orcid.org/0000-
0001-6004-9373 (P. Calistri); https://orcid.org/0000-0002-7547-1195 (C. Camma); https://orcid.org/0000-0002-0864-464X (K. Maassen)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
1. Introduction
The microbiological surveillance and outbreak investigation are today supported by public health
organizations through genomics-based information systems integrating multiple metadata related to
foodborn disease biosample collected over the past few decades for sequencing by veterinarians,
biologists, researchers and medical doctors [1]. Unfortunately, these metadata associated to samples
are, neither organized nor harmonized between public health organizations in charge of food,
veterinary and environmental sectors [2]. Consequently, several projects of the food [3], veterinary [4]
and environmental [5] sectors, aim currently at organizing and harmonizing these metadata based on
developments and implementations of ontologies.
Both in and between European countries, the European Joint Programme (EJP) “One Health
Structure In Europe” (COHESIVE) is of paramount importance for organizations of food production
systems, as well as the veterinary and human health domains, in dealing with (re‐)emerging zoonoses,
including antimicrobial resistance and food‐borne zoonoses 2. Because of current implementation of
omics for outbreak investigation, source attribution and risk assessment of food-borne
microorganisms across European Member States [6], the EJP COHESIVE, initially developed to
collect data related to the area of risk‐analysis, aims today at integrating also genomics data from
human and veterinary domains involved in genomics-based surveillance (Figure 1).
Figure 1: Actors and data types of the “COHESIVE information system” (CIS) supported by the
European Joint Programme (EJP) “One Health Structure In Europe” (COHESIVE)
2
EJP COHESIVE
Home: https://onehealthejp.eu/jip-cohesive/
In the framework of the EJP COHESIVE, the “COHESIVE information system” (CIS) has been
developed by IZSAM and three demo versions have been provided to organizations from Italy,
Norway and The Netherlands (Task 4.1) to integrate pathogen information from public health, animal
health and food safety surveillance at Member State level, integrating metadata related to samples (i.e.
isolation date, origin, matrix) and genomics analyses (i.e. genome assembly, mapping of reads,
species identification, mutations of interest) [7–9].
The first challenge of the CIS is to harmonize the past food terms which have been accumulated in
different languages from different European organizations over the past few decades, though
free-systems of recording independently of food term ontology. The second challenge of the CIS is to
allow recording by European organizations of future food terms following a common ontology of
food terms. A common and unified ontology of food terms into the CIS would allow queries from past
and future food terms recorded by different European organizations (e.g. Which samples related to the
cheese factory sector were isolated during 2008 in Italy with a clonal complex CC8?).
In parallel, the EJP “One health suRveillance Initiative on harmOnization of data collection and
interpretatioN” (ORION) (WP3) focused on the development of a “Health Surveillance Ontology”
(HSO) at the European level [10], while other international consortia developed “Open Biological and
Biomedical Ontology” (OBO) foundries [11], such like the “Genomic Epidemiology Ontology”
(GenEpiO) [12] and the “food ontology” (FoodOn) [13]. Among these ongoing projects, the FoodOn
ontology fit particularly well requirements of the CIS concerning the need of unified food terms
(Figure 2), while HSO, OBO, GenEpiO focus mainly on surveillance system level data (e.g. number
of samples collected, cases observed, ...), development of interoperable ontologies for the biological
sciences (e.g. chemical entities, human disease, gene ontology, phenotype and trait, …), as well as
vocabulary necessary to identify, document and research foodborne pathogens (e.g. genomic
laboratory testing, specimen and isolate metadata), respectively.
Figure 2: FoodOn: a harmonized food ontology to increase global food traceability, quality control
and data integration [13]
The specific objectives of the presented CIS Version 2 (CISv2) are (Figure 3):
1 the harmonization of the past food terms into the CISv2 which we hope to achieve using text
mining tool LexMapr and FoodOn ontology from the CIS (i.e. Action 1),
2 the recording of the future food terms into the CISv2 which we hope to achieve implementing
FoodOn ontology into the CIS through the graph-database Neo4j (i.e. Action 2),
3 the discussion of future actions related to food ontology harmonization between organizations
which we hope to achieve implementing the CISv2 in different organizations (i.e. Action 3),
4 and the discussion of future improvements related to queries from the CISv2 which we hope
to achieve combining relational- (CIS) and graph-database (CISv2) relationships (i.e. Action 4).
CHALLENGES OBJECTIVES S OLUTIONS NEW S YS TEM
Action 1 Food On
h a rm on iza tion of
th e p a s t food te rm s Le xM a p r +
Food
O n t o lo g y
Action 2
CIS CIS v2
re c ordin g of th e
fu tu re food te rm s +
C O H E S IV E
C O H E S IV E
In f o r m a tio n
In f o r m a tio n
S y s te m
S y s te m Action 3 Ve r s io n 1
Ve r s io n 1
h a rm on iza tion - in e roga ble c om m on
- dif fe re n t la n gu a ge s im p le m e n ta tion in s e v e ra l la n gu a ge s
- dis ha rm on iou s food be tw e e n
org a n iza tion s - ha rm on iou s food
te rm s org a n iza tion s te rm s
- a bs e n c e of G e n e
- ha rm on iou s G e n e
On tology te rm s
On tology te rm s
Action 3
im p rov e m e n ts c om bin a tion of re la tion a l- a n d
re la te d to q u e rie s g ra p h -da ta ba s e re la tion s h ip s
Figure 3: Challenges, objectives, solutions and future new version (CISv2) of the “COHESIVE
information system” (CIS) supported by the European Joint Programme (EJP) “One Health Structure
In Europe” (COHESIVE)
2. Material and Methods
The harmonization of the past food terms from the CIS (i.e. Action 1) and implementation of the
FoodOn ontology into the CIS (i.e. Action 2) are required before performing any actions related to
food ontology harmonization between organizations (i.e. Action 3) and improvement of queries from
the CISv2 (i.e. Action 4).
2.1. Action 1: Harmonization of Past Food Terms from the CIS based on the
Food Ontology FoodOn
The CIS terms come from different providers and were manually curated by each involved
organization from Italy, Norway and The Netherlands. The lists of food terms from several
organizations written in different languages (i.e. Italy, Norway, Netherlands) were translated by each
organization into lists of food terms in English without independent verification of the translation to
avoid bias or error (Figure 4). This English translation of multilingual food terms was done with
Google translate. Then, these lists of English food terms were mapped against the food ontology
FoodOn [13] with the rule-based text mining tool LexMapr [14]. More precisely, LexMapr uses a
rule-engine for handling synonyms, prefixes and suffixes to automatically map a matrix of English
food terms with FoodOn codes. The resulting harmonized food terms were finally imported in the
CISv2 (Figure 4). The accuracy of LexMapr mapping and missing food terms from FoodOn were not
assessed in the present study.
• New source “FoodOn”
C IS Lis t of
Lis t of
C O H E S IV E
Te rm s in
E n g lis h
• New “FoodOn code” as
dif fe re n t
In f o r m a t io n
la n g u a g e s
te rm s matrix term with FoodOn
S y s te m
Ve r s io n 1 source
• Association between the
term and the FoodOn code
Le xM a p r
Food On Lis t of C IS v 2
E n g lis h < te r m , Foo d On
te rm s C O H E S IV E
code> In f o r m a t io n
Food on tolog y
O n t o lo g y a s s oc ia tion s S y s te m
Ve r s io n 2
Figure 4: Harmonization of food terms from the CIS based on the food ontology FoodOn with the
objective to harmonize the past food terms of samples accumulated over the past few decades into
the CISv2
2.2. Action 2: Implementation of the FoodOn Ontology into the CIS to Allow
Recording of Future Food Terms from the CISv2
The implementation of the FoodOn ontology into the CIS was performed with the graph-database
Neo4j [15] (Figure 5). The graph-database Neo4j is able to reveal invisible contexts and hidden
relationships, storing and traversing networks of highly connected data. In the present context, Neo4J
transforms xml specifications of the FoodOn ontology [13] into a graph-database of food terms. Using
open source technologies 3, the FoodOn ontology was imported into Neo4j in order to build the first
iteration of the knowledge graph. From the resulting CISv2, the Apache Zeppelin notebook [16] was
used to create a data pipeline that covers from the ingestion, the enrichment, to the visualization of
queries performed on the FoodOn ontology through Neo4J [15] (Figure 5). The Apache Zeppelin
notebook is a Web-based notebook that enables data-driven, interactive data analytics and
collaborative documents with SQL and other languages.
C IS
C O H E S IV E
In f o r m a tio n
S y s te m
Ve r s io n 1
C IS v 2
C O H E S IV E
In f o r m a tio n
S y s te m
Food On Ve r s io n 2
Food
O n t o lo g y
Figure 5: Implementation of FoodOn ontology into the CIS with the objective to allow future records
of food terms into the CISv2 based on the food ontology FoodOn
3
Open source technologies
GitHub: https://github.com/neo4j-contrib/neo4j-apoc-procedures and https://github.com/neo4j-labs/neosemantics
3. Results
The tools for the harmonization of the past food terms into the CIS (i.e. Action 1) and
implementation of the FoodOn ontology into the CIS (i.e. Action 2) were carefully selected according
to future other actions related to food ontology harmonization between organizations (i.e. Action 3)
and improvement of queries from the CISv2 (i.e. Action 4).
3.1. Action 1: Harmonization of Past Food Terms from the CIS based on the
Food Ontology FoodOn
Compared to nutritional ontologies designed to annotate and describe intervention trials [17–19],
the FoodOn ontology [13] was selected because of its strong representation of food nutrients and
processing [20]. Compared to other text mining tools in Literature [21], finance [22] and medicine
[23], the selection of the rule-based text mining tool LexMapr to map food terms translated in English
against the FoodOn ontology (Table 1) was driven by its interoperability across sectors [14]. Initially
developed to fulfil objectives of public health surveillance networks like the US FDA’s GenomeTrakr
system and the US National Antimicrobial Resistance Monitoring System (NARMS), LexMapr
describes indeed food pathogen source for reporting of transmission dynamics in public health
foodborne pathogen surveillance and investigation 4. Without speaking about issues related to English
translations, the proposed mockup (Table 1) shows that there is a need to improve FoodOn's curation
because FoodOn does not have a term for a generic pizza with or without meat or cheese 5.
Table 1
Mockup of food terms from the CIS harmonized through LexMapr-based mapping against the
FoodOn ontology with the objective to harmonize the past food terms of samples accumulated over
the past few decades into the CISv2
Code from
Matrix FoodOn Code* Source#
providers
Pizz ka a muzzarel e a pummarol
Code-1 FOODON_000102 Naples coding system
ngopp
Code-2.1 Pizza Margherita FOODON_000102 ISO
Code-2 Pizza FOODON_000001 ISO
* #
FoodOn codes are arbitrary examples ISO means that samples from the present example
follow the requirements of the International Organization for Standardization
4
Open source text mining tools
LexMapr: https://www.cineca-project.eu/blog-all/lexmapr-a-rule-based-text-mining-tool-for-ontology-term-mapping-and-classification
5
FoodOn term "pizza food product"
URL: https://urlsand.esvalabs.com/?u=http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FFOODON_03310775&e=2f9e67d3&h=88f8ff14&
f=n&p=y
3.2. Action 2: Implementation of the FoodOn Ontology into the CIS to Allow
Recording of Future Food Terms from the CISv2
The implementation of the FoodOn ontology into the CIS was successfully performed through
Neo4J [15], as exemplified with the Apache Zeppelin notebook (Figure 6) [16]. The graph-database
Neo4j was selected for its capacity to query easily ontologies [15].
Figure 6: Zeppelin browser allowing to navigate trough and query the FoodOn ontology
implemented into the CIS through Neo4j with the objective to allow future records of food terms
into the CISv2 based on the FoodOn ontology
4. Discussion
4.1. Action 3: Harmonization of Food Terms and Implementation of the
FoodOn Ontology in Public Health Organizations
In a near future, we plan to harmonize the past food terms from the CIS (i.e. Action 1) and
implement the FoodOn ontology into the CIS (i.e. Action 2) instances provided during the feasibility
study for the organizations: IZSAM (Italy), NVI (Norway) and RIVM (The Netherlands) at the time,
in order to use the same CISv2 implementing an identical food ontology. Indeed, different coding
systems can be harmonized looking for different English translated terms that LexMapr [14] maps
towards the same FoodOn codes [13] (Table 1). Even if LexMapr mapping of English translated terms
against the FoodOn ontology may not be completely efficient, it allows sharing of common FoodOn
terms derived from English translated terms between organizations using different languages.
4.2. Improvement of Queries from the CISv2
Today, the CIS without Neo4j implementation (Figure 4) can be interrogated through
relational-database queries for data related to isolation date, sampling origin, food matrix and/or
genomics data, while the CISv2 implementing Neo4j [15] can be interrogated through queries of
graph-database relationships for food sectors organized inside the FoodOn ontology [13]. We plan in
a near future to improve queries combining queries of relational- and graph-database relationships in
order to interrogate the CISv2 for isolation date, sampling origin, food matrix, genomics data and/or
food sectors organized inside the FoodOn ontology. Just using the CIS, we can query samples via
standard SQL statements ().
--------------- SQL QUERY on sampling and genomics metadata ----------
select samples where
sampling_date is 2018 and
sampling_place is Italy and
CC=CC8
Draft code 1: SQL query using relational-database relationships of the CIS (response: samples
isolated during 2008 in Italy with a clonal complex CC8)
Neo4J implemented into the CISv2 allows query of FoodOn terms using graph-database
relationships (). If the queried FoodOn term do not exist, it would become a new Environment
Ontology (ENVO) term useful for FoodOn curation.
--------------- Neo4J QUERY on FoodOn Terms --------------------------
select foodoncode
with a relationship to foodoncode of "cheese factory"
Draft code 2: Neo4J query using graph-database relationships of the CISv2
(response: FoodOn codes related to the FoodOn term "cheese factory")
Using the combination of SQL and Neo4J queries, an innovative query could use relational- and
graph-database relationships ().
(
--------------- SQL QUERY on sampling and genomics metadata ----------
select samples where
sampling_date is 2018 and
sampling_place is Italy and
CC=CC8
) AND foodoncode in (
--------------- NEO4J QUERY on FoodOn Terms --------------------------
select foodoncode
with a relationship to foodoncode of "cheese factory"
)
Draft code 3: SQL and Neo4J queries using relational- and graph-database relationships of the CISv2,
respectively (response: samples isolated during 2008 in Italy with a clonal complex CC8 and a
FoodOn code related to the FoodOn term "cheese factory")
Adding additional ontologies, such as the Gene Ontology (GO) dedicated to GO terms [24, 25]
describing the metabolic pathways 6, other innovative combinations of SQL and Neo4J queries could
use relational- and graph-database relationships filtering samples via classical relational constraints,
and adding ontology constraints on food matrix and genome annotations (). This kind of Gene
Ontology-based query is typically usefull to list GO-terms from a subset of genomes and a larger
collection of genomes in order to perform a Genome Ontology Enrichment Analysis (GOEA) [26]
identifying over-represented metabolic pathways across genomes of interest (e.g. genomes involved in
an outbreak).
(
--------------- SQL QUERY on sampling and genomics metadata ----------
select samples where
sampling_date is 2018 and
sampling_place is Italy and
CC=CC8
) AND foodoncode in (
--------------- NEO4J QUERY on FoodOn Terms --------------------------
select foodoncode
with a relationship to ontology_node of "cheese factory"
) AND GOterm in (
--------------- NEO4J QUERY on GO Terms ------------------------------
select GOterm
with a relationship to ontology_node of "ATPase activity"
)
Draft code 4: SQL and Neo4J queries using relational- and graph-database
relationships of the CISv2, respectively (response: samples isolated during 2008
in Italy with a clonal complex CC8, a FoodOn code related to the FoodOn term
"cheese factory" and a Gene Ontology code related to the Gene Ontology term
"ATPase activity")
6
Open source ontology
GENE ONTOLOGY: http://geneontology.org/docs/download-ontology/
4.3. Significant Overlapping with Existing Efforts
The present development of the CISv2, dedicated to food ontology between organizations,
overlaps significantly to existing efforts in the field of human nutrition, especially the food, health,
nutrition domain ontologies (FHNDO) [17], the Ontology for Nutritional Studies (ONS) [18], and the
Ontology for Nutritional Epidemiology (ONE) [19]. While these nutritional ontologies aim at
identifying healthy diets based on interoperability of ontologies related to classifications of diets,
diseases and food [17–19], the CISv2 is dedicated to surveillance and investigation of foodborne
outbreaks in human at the genomic scale based on ontologies related to genes and food. Instead of
identifying healthy diets (i.e. FHNDO, ONS and ONE), the immediate goal of the project is to explain
genetically foodborne outbreaks in human (i.e. CISv2). Compared to the relational-databases
dedicated to the nutritional ontologies FHNDO [17], ONS [18] and ONE [19], the CISv2 presents the
advantage to be able to combine relational- (i.e. CIS) and graph-databases (i.e. Neo4j).
5. Conclusion
The harmonization of the past food terms into the CIS (i.e. Action 1) and implementation of the
FoodOn ontology into the CIS (i.e. Action 2) will allow harmonization of the food ontology between
organizations (i.e. Action 3) and improvement the interrogation of the CISv2 (i.e. Action 4)
combining queries from relational- (i.e. CIS) and graph-databases (i.e. Neo4j). Thenceforth, the
CISv2 need FoodOn curators from the ontology community to perform better biosample description,
text mining and text mashing to ontology terms. In the longer term, we also plan to extend the CISv2
to other ontologies, like Gene Ontology in order to perform GOEA. Based on successful outcomes of
actions 1 and 2, the CISv2 presents today harmonious English food terms and can be distributed for
easy implementation in different European organizations with standard servers (Action 3) and used to
perform combinations of queries from relational- (i.e. past food terms from CIS) and graph-databases
(i.e. future food terms from CISv2).
6. Acknowledgements
The study was funded by the European Joint Programme (EJP) “One Health Structure In Europe”
(COHESIVE). Mention of trade names or commercial products in this article is solely for the purpose
of providing specific information and does not imply recommendation or endorsement by the IZSAM.
The authors declare that they have no competing interests and thank especially the Italian Ministry of
Health for supporting in the acquisition of high-performance computing resources. This manuscript
was drafted by Nicolas Radomski based on an available Word template under a Creative Commons
License Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).
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