=Paper=
{{Paper
|id=Vol-3659/IJCKG_2023_P1
|storemode=property
|title=Toward the Construction of a Knowledge Graph from Japanese Food Ontology for the Prevention of Frailty
|pdfUrl=https://ceur-ws.org/Vol-3659/IJCKG_2023_P1.pdf
|volume=Vol-3659
|authors=Chihiro Higuchi,Agustin Martin-Morales,Ai Oya,Mai Inoue,Misako Ikkai,Kenji Mizuguchi,Michihiro Araki
|dblpUrl=https://dblp.org/rec/conf/jist/HiguchiMOIIMA23
}}
==Toward the Construction of a Knowledge Graph from Japanese Food Ontology for the Prevention of Frailty==
https://ceur-ws.org/Vol-3659/IJCKG_2023_P1.pdf
Toward the Construction of a Knowledge Graph from
Japanese Food Ontology for the Prevention of Frailty
Chihiro Higuchi1,* , Agustin Martin-Morales1 , Ai Oya1 , Mai Inoue1 , Misako Ikkai1 ,
Kenji Mizuguchi2,1 and Michihiro Araki1
1
Artificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutes of Biomedical
Innovation, Health and Nutrition (NIBIOHN)
2
Institute of Protein Research (IPR), Osaka University
Abstract
The Osaka Prefectural Government has formulated an inspection method for the four elements of frailty:
nutrition, body function, oral cavity, and social activity decline. Since nutrition is primarily influenced
by food intake, developing and utilizing a food ontology is essential for scientific research on frailty.
Recent studies have also elucidated the relationship between food, gut bacteria, and disease. Foods are
transformed into nutrients through metabolism, and in the process, there are changes in various genes
and proteins, which are also associated with diseases in relation to gut bacteria. We aim to contribute to
better prevention of frailty by constructing a knowledge graph consisting of these elements.
Keywords
Knowledge graph, Ontology, Food ontology, Frailty, Sarcopenia
1. Introduction
Nutrition is a key component of good health. However, many individuals may face challenges
in obtaining sufficient nutrition due to factors that affect their health, such as allergies. To
conduct nutritional research that yields reliable and comparable results, it is necessary to use
uniform terminology and appropriate food descriptions. Consequently, there is a demand for a
computer-readable Japanese food ontology that can accurately represent the characteristics and
relationships of various foods.
Frailty is a condition of vulnerability and decreased physiological reserve in older adults.
Sarcopenia is a disorder characterized by the loss of muscle mass and strength[1]. A survey of
middle-aged and older adults aged 40 and over living in Settsu City, Osaka Prefecture, revealed
that a certain proportion of them had frailty or sarcopenia even in their 40s and 50s. Since
IJCKG 2023: The 12th International Joint Conference on Knowledge Graphs, Decemner 08–09, 2023, Miraikan - The
National Museum of Emerging Science and Innovation, Tokyo, Japan.
*
Corresponding author.
†
These authors contributed equally.
$ higuchi@nibiohn.go.jp (C. Higuchi); agustinmartinmorales@nibiohn.go.jp (A. Martin-Morales);
a.oya@nibiohn.go.jp (A. Oya); m.inoue@nibiohn.go.jp (M. Inoue); ikkai@nibiohn.go.jp (M. Ikkai);
kenji@nibiohn.go.jp (K. Mizuguchi); araki@nibiohn.go.jp (M. Araki)
0000-0003-2808-2813 (C. Higuchi); 0000-0002-3564-4776 (A. Martin-Morales); 0000-0003-3204-1627 (M. Inoue);
0000-0003-3021-7078 (K. Mizuguchi); 0000-0002-6686-4018 (M. Araki)
© 2023 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
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
frailty and sarcopenia may affect not only the elderly but also the working-age population, early
prevention is necessary. To prevent frailty and sarcopenia, it is important to improve lifestyle
habits such as diet and exercise. People who perceive themselves as heavier than they actually
are may have low muscle mass, which should be taken into account.
A knowledge graph with guidelines derived from previous frailty prevention studies is
expected to contribute to more accurate frailty prevention. Genome-wide association studies
(GWAS) have identified single nucleotide polymorphisms (SNPs) associated with frailty. The
presence of SNPs suggests that individual differences in frailty prevention may occur. It has
also been reported that the gut microbiota environment, which is influenced by food intake,
is involved in the expression of messenger RNAs (mRNAs) and microRNAs (miRNAs), and
vice versa, the expression of mRNAs and miRNAs, which are influenced by food intake, affects
the gut microbiota environment. This suggests that the possible knowledge graph for frailty
prevention can be very complex with other factors.
2. Methods
Weakness is said to be caused by the decline of four functions: nutrition, body function, oral
cavity, and social activity. In Osaka Prefecture, the assessment includes whether individuals
can form a loop with the thumb and index finger of both hands, and whether they can stand up
from a chair on one leg. Whether or not the patient has a complete meal with staple food, main
dishes, and side dishes. Whether or not the patient swallows when eating or drinking tea or
soup. Whether or not the patient goes out once a week. The checklist consists of the following
five items. The knowledge graph is created according to this list.
The above mentioned indicators of frailty diagnosis include food, necessitating a Japanese
food ontology that cabe processed mechanically. FoodOn[2] is available for food ontology, but it
lacks coverage of a common foods in Japan. We employed the Web Ontology Language (OWL) to
describe the NHNS data, utilizing its hierarchical classification scheme and appropriate Uniform
Resource Identifiers (URIs). This ontology is published as an alpha version of FGNHNS[3] on
BioPortal (https://bioportal.bioontology.org/ontologies/FGNHNS). We use food names in this
ontology to link foods to nutrients.
Associations between food, gut bacteria and disease were extracted from the databases listed
below.
• Disease name and DOID from Disease ontology
(https://disease-ontology.org/)
• Gut bacteria name and NCBI ID from NCBI taxonomy
(https://www.ncbi.nlm.nih.gov/taxonomy)
• Gut bacteria disease interaction type from gutMDisorder database
(http://bio-annotation.cn/gutMDisorder/)
• Gut bacteria edge type and weight from MIND database
(http://www.microbialnet.org/mind_home.html)
Figure 1: Classes view of FGNHNS registered on BioPortal. (https://bioportal.bioontology.org/
ontologies/FGNHNS).
3. Result
We are building a knowledge graph using the constructed food ontology and various other
factors related to frailty, but since there is some cohort data dependence, we have not yet reached
a concrete inference. The constructed knowledge graph schema is Figure 2. We proposed that
individual differences in genes that vary with food metabolism may influence frailty.
4. Discussion and conclusion
The four factors that traditionally define frailty are body function, social activity, oral cavity,
and nutrients. A food component was added because nutrients are obtained through food
metabolism, but food intake also causes variation in genes and gut bacteria, which affect frailty
in terms of disease improvement. Individual differences due to genetic variants must also be
taken into account. In addition, the factor of sleep should also play a role in frailty, although
cohort data may not be sufficient. Therefore, the knowledge graph on frailty could be further
Figure 2: A knowledge graph schema to describe frailty
complicated, as shown in Figure 2, and could contribute to precise frailty prevention efforts. It is
difficult to predict frailty using the knowledge graph with only a database of known reports, and
additional data is needed. These are still in the process of being built and the various processes
listed in future work need to be implemented. As this system matures, it is expected to elucidate
the various associations and mechanisms between food and disease.
5. Acknowledgments
We thank to Dr. Tatsuya Kushida (RIKEN), Assist. Prof. Chioko Nagao (IPR), Assoc. Prof.
Hideki Hatanaka (DBCLS), Prof. Kouji Kozaki (OECU), a collaborator in the development of
the FGNHNS, and the members of the Artificial Intelligence Center for Health and Biomedical
Research (ArCHER).
References
[1] Watanabe D et al. Factors associated with sarcopenia screened by finger-circle test among
middle-aged and older adults: a population-based multisite cross-sectional survey in Japan.
BMC Public Health. 2021 Apr 26;21(1):798. doi: 10.1186/s12889-021-10844-3. PMID: 33902521;
PMCID: PMC8074487.
[2] Dooley DM et al. FoodOn: a harmonized food ontology to increase global food traceability,
quality control and data integration. NPJ Sci Food. 2018 Dec 18;2:23. doi: 10.1038/s41538-
018-0032-6. PMID: 31304272; PMCID: PMC6550238.
[3] Chihiro H et al. Japanese Food Ontology. IFOW 2022 Integrated Food Ontology Workshop.
August 15-19, Jönköping, Sweden.