Toward the Construction of a Knowledge Graph fromJapanese 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

Food is an important component of human health. We are working on a prototype of a Japanese foodontology to study the intake of nutrients through diet. As an application of this ontology, a knowledge
graph contributing to the prevention of frailty was studied. Although traditional diagnostic methods forpreventing frailty have been established, more accurate prevention of frailty is expected to be possible
by taking into account variations in intestinal bacteria, genes, and proteins due to food intake, as well as
individual differences in genetic polymorphisms.

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 acomputer-readable Japanese food ontology that can accurately represent the characteristics andrelationships of various foods. We therefore developed a prototype ontology of Japanese food(Figure 1) based on the food groups listed in the National Health and Nutrition Examination
Survey published by the Ministry of Health, Labour and Welfare of Japan. This ontology can beused for various health-related research topics, one of which is the prevention of frailty.
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. A survey ofIJCKG 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).
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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
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), andvice 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. Result
The constructed knowledge graph schema is Figure 2.
The red dashed line represents the conventional diagnosis of frailty. This figure shows thevarious factors that influence frailty, including food and other diagnostic criteria. Food isdivided into different nutrients by using an ontology that we are developing. Food intake affects
the changes in genes, proteins, and gut microbiota. There are also interactions among genes,
proteins, and gut microbiota, which can vary depending on the individual. For example, genetic
polymorphisms can cause individual differences in genes, which can affect the susceptibility todiseases and the other components of frailty diagnosis.

3. Discussion
This study only prototyped a knowledge graph for preventing frailty, and whether this one will
be effective or not needs to be demonstrated using various data from the actual cohort.

4. 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 ofthe FGNHNS, and the members of the Artificial Intelligence Center for Health and Biomedical
Research (ArCHER).

Figure 1: Classes view of FGNHNS registered on BioPortal. (https://bioportal.bioontology.org/ontologies/FGNHNS).

Figure 2: Developed a knowledge graph schema to prevent frailty