Recipe Recommendation for Health Care Using Food
Functionality Knowledge Graph and Probabilistic Logic
Programming
Akio Kobayashi

Akira Hashimoto

akio.kobayashi@naro.go.jp
National Agriculture and Food Research Organization
Tsukuba, Ibaraki, Japana.hashimoto@snlab.cs.tsukuba.ac.jp
Unibersity of Tsukuba
Tsukuba, Ibaraki, Japan

Tetsuo Katsuragi

Takahiro Kawamura

National Agriculture and Food Research Organization
Tsukuba, Ibaraki, Japan

National Agriculture and Food Research Organization
Tsukuba, Ibaraki, Japan

ABSTRACT

1

Daily dietary choices play an indispensable role in mitigating lifestylerelated diseases. Functional components found within foods may
offer potential benefits in this regard. We constructed a knowledge
graph that links these functional components of food to recipes
and further developed a recommendation system to suggest dishes
that may contribute to the alleviation of lifestyle-related diseases.
Since dietary requirements can vary significantly depending on thespecific disease and individual conditions, combining these requirements forms a vast probabilistic distribution. Our proposed system
makes recommendations based on the dietary requirements specific to the disease by employing probabilistic logical programming,
which utilizes a knowledge graph about food and information about
the user’s condition. Meanwhile, nodes in the knowledge graph,such as food functionalities or recipes, are characterized by theirrelationships with other surrounding nodes, such as food ingredients. To handle and solve these characteristics, we employ the
deep learning probabilistic logical program. And the result of theexperimentation, we have constructed a system that recommends
recipes effective for lifestyle-related diseases based on functional
ingredients.

Today, lifestyle-related diseases have become a significant publichealth issue globally. According to statistics published by the World
Health Organization (WHO) in 2022[15], there is an increasing
trend in deaths caused by non-communicable diseases. Notably,
deaths due to diabetes have surged by 70% between 2000 and 2019,emphasizing the urgent need for measures against lifestyle-relateddiseases worldwide. A primary cause of these diseases is found to
be closely linked to daily dietary habits. In 2020, the WHO released
five guidelines for preventing lifestyle-related diseases, emphasizing the importance of revisiting dietary ingredient balance andavoiding excessive intake of salt, fat, and carbohydrates. Given thisbackdrop, there has been a recent surge in the provision of daily
food-logging tools and health-improving recipe recommendation
systems[2, 11, 14]. Most of these systems determine recipe recommendations based on the essential nutritional content of meals anduser metrics like BMI. On the other hand, specific foods possess
functional properties that can promote health, including mitigating lifestyle-related diseases. However, databases organizing thisfunctional information or systems utilizing it have yet to be proposed, and it remains underutilized in recipe recommendations. In
this study, we collate and aggregate various functional component
databases, linking items in food and recipe databases to constructa knowledge graph, thereby systematizing information on foodfunctionality. In addition to storing this knowledge graph in our
NARO-linked databases [10], we also propose a method of using
this knowledge graph to recommend recipes that are effective in improving lifestyle-related diseases. Recognizing that the necessaryfunctionalities, nutrients, physical and mental conditions differ
depending on the type of lifestyle disease and individual users,
our approach implements a probabilistic logical inference. Thismethod is considered apt as it probabilistically represents whether
to apply specific conditions based on user and meal characteristics.
This research establishes recommendation rules targeting obesity,
a lifestyle-related disease, and implements these recommendations
using the constructed knowledge graph and inference system.
And as a result of the experimentation, we have constructeda system that recommends recipes effective for lifestyle-relateddiseases based on functional ingredients.

KEYWORDS
Knowledge Graph, Probabilistic Logic Programming, Health CareACM Reference Format:
Akio Kobayashi, Akira Hashimoto, Tetsuo Katsuragi, and Takahiro Kawamura. 2018. Recipe Recommendation for Health Care Using Food Functionality Knowledge Graph and Probabilistic Logic Programming. In Proceedingsof he 12th International Joint Conference on Knowledge Graphs (IJCKG ’23).
ACM, New York, NY, USA, 8 pages. https://doi.org/XXXXXXX.XXXXXXX

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IJCKG ’23, December 08–09, 2023, Tokyo, Japan© 2018 Association for Computing Machinery.
ACM ISBN 978-1-4503-XXXX-X/18/06. . . $15.00
https://doi.org/XXXXXXX.XXXXXXXINTRODUCTIONIJCKG ’23, December 08–09, 2023, Tokyo, Japan

2

RELATED WORK

Numerous systems for recipe recommendation that utilize databases,
knowledge graphs, and ontologies related to food have been proposed. Many of these aim to recommend recipes tailored to users’
profiles. While not all of these systems explicitly aim to improve
lifestyle-related diseases, most take into consideration the balanceof food and indirectly aim to recommend recipes that could improve
lifestyle-related diseases.
To the best of our knowledge, there are no recommendation
systems that focus on the functionality of foods. However, there arerecipe recommendation systems that reference knowledge graphs,
such as those that analyze texts about recipes and users’ healthconditions[11, 14], and those that optimize various necessary parameters for recommendation based on a knowledge base throughmachine learning[1, 2, 21].
Furthermore, when considering health in recipe recommendations, multiple systems [3, 7, 17, 20] have been proposed that primarily standardize rules based on intake limits of essential nutrientsset by each country’s responsible ministries. These systems also
consider users’ preferences when making recommendations.
In particular, Chen et al. [3] proposes a multi-task learning
method that combines a knowledge base, users’ eating history,
and intake limits of essential nutrients. This method simultaneously learns the recipes that a user can intake and the preferences
derived from their eating history.
Other relevant to this study are systems like the one in the HEALs
project1 that considers the user’s health condition and allergies[11],and systems that identify user profiling through food preferences
to recommend recipes[1].
In this study, we aim to encourage a revision of dietary habits asa preventive measure when individuals exhibit physical measurements that suggest a potential risk for lifestyle-related diseases. To
realize this, it is essential to be able to set conditions to be consideredin dietary habits for each corresponding lifestyle-related disease.
Moreover, the importance of these conditions varies depending on
individual characteristics such as constitution; thus, they should
be applied probabilistically. Therefore, to adopt recommendation
rules suitable for each lifestyle-related disease, we employed an
approach based on probabilistic logical inference grounded in rulebased systems.
The knowledge bases used in these studies often include largescale food ontologies like FoodOn[5], FoodKG[11], large-scale recipe
data like Recipe1M+[13], and extensive databases of food compounds like FlavorDB[8], FooDB2 . None of these databases focus onfood functionality. We believe that combining these with databases
on functional food components, such as the proprietary databasefrom the National Agriculture and Food Research Organization3 ,or the Consumer Affairs Agency’s functional food notification information search service4 , would enable the recommendation of
more effective recipes for lifestyle-related diseases.

1 https://idea.rpi.edu/research/projects/heals

Kobayashi et al.

3

SYSTEM OVERVIEW

The overview of our system construction approach is illustrated in
Figure 1. Initially, we constructed a knowledge graph by integrating
a dataset related to functional components that we gathered online
with existing datasets on recipes and foods. For the existing recipe
dataset, we utilize a structured dataset from Recipe1M+, a largescale recipe dataset that contains recipes, ingredients, respective
nutritional components, their quantities, and cooking steps. The
rationale for selecting this dataset is the same as other existing
recommendation systems: nutritional balance is the primary focus
for improving lifestyle-related diseases. Therefore, it is essential tochoose a dataset where the amount of each nutrient is explicitly
specified. For the data on functional components, we used datacollected from the food functionality database published by the National Agriculture and Food Research Organization5 and the searchservice for functional notification products provided by the Consumer Affairs Agency6 . While many of these functional component
data are linked to compounds, the search service of functional notification products is not always linked to specific foods. As a result,
we first constructed a temporary knowledge graph by associatingthese data with foods and compounds in FlavorDB, a comprehensive
database on food compounds. Subsequently, by linking this knowledge graph with the structured data from Recipe1M+, we built aknowledge graph embedded with information on food functionality
and nutritional components.

Figure 1: System Overview.
To utilize this knowledge graph, we constructed separate user
data for 1,000 individuals to perform recipe recommendations. Thisdataset includes information on the user’s height, weight, bodyfat percentage, blood and urine test results, and approximate dataon the nutritional components they consume daily. This dataset
allows us to gauge whether a user tends to have lifestyle-relateddiseases and if there is an excess or deficiency of specific nutrients,

2 www.foodb.ca
3 https://nousanbutsu-kinou.rad.naro.go.jp/

5 https://nousanbutsu-kinou.rad.naro.go.jp/

4 https://www.fld.caa.go.jp/caaks/cssc01/

6 https://www.fld.caa.go.jp/caaks/cssc01/

Recipe Recommendation for Health Care Using Food Functionality Knowledge Graph and Probabilistic Logic Programming

IJCKG ’23, December 08–09, 2023, Tokyo, Japan

matching from Compound to Function was challenging with FlavorDB, and only one-to-one correspondences could be obtained. As
a result, the number of nodes in the function class and the numberof edges from the compound class are the same.

4.1

Figure 2: Proposed ontology for recipes, foods and its functionalities.
Table 1: Knowledge graph instances.
Instance typeamountRecipeFood (Ingredient)
Nutrient
CompoundFunction
Edge (Recipe to Food)
Edge (Recipe to Nutrient)
Edge (Food to Nutrient)
Edge (Food to Compound)
Edge (Food to Function)
Edge (Compound to Function)

51,235357
660,493
61
311,435
307,4102,142
1,868,610
73
61

encompassing most of the information necessary for recipe recommendations. By referencing this data alongside the data on theknowledge graph using a probabilistic logical reasoning program,we can achieve recommendations for recipes effective in improving
lifestyle-related diseases and express the importance of conditions
to consider during recipe selection regarding probability values. It
is anticipated that this approach can be utilized in applications likealternative recipe recommendations or the development of otherrecipes.

4

KNOWLEDGE GRAPH CONSTRUCTION

The knowledge graph proposed in this study is defined by an ontology consisting of classes of recipes, ingredients, nutritional components, compounds contained in ingredients, functional, and edges
indicating their respective relationships. This ontology is illustrated
in Figure 2. Additionally, the number of instances for each classand edge is presented in Table 1.
Each edge is directed, and while edges representing inverse relationships are also included, they are omitted in Table 1 and in
the ontology representation in Figure 2 for simplicity. Furthermore,

Utilized Datasets

4.1.1 Recipe1M+. Recipe1M+ is a large-scale dataset of recipes andimages constructed for the task of retrieving recipes from images,
and as the name suggests, it contains over 1 million recipes. These
recipes were collected from online recipe-sharing sites, and mostof the recipe documents are published in a format that simplifies
the parsing of HTML. Among them, some data is released in a
structured format based on the nutritional components provided
by the USDA, including the amount of nutritional components
contained and the amount used during cooking. Given that this is
likely the largest dataset with structured data on the nutritional
content and quantities used in recipes, we have adopted a structure
centered on this data in our knowledge graph.
4.1.2 NARO Food Function Database. The National Agriculture
and Food Research Organization provides a search service for functional food components based on their unique research7 , whichcontains data on 200 types of foods and 30 functional ingredients.
While results on the content of various functional ingredients arealso provided, the data is in text format and does not have a consistent description style. Therefore, in this study, only the relationships
between the foods, their functionalities, and the compounds withthese functionalities are used.
4.1.3 Functional Labeling Food Notification Information Search Service. This is a web service8 provided by Japan’s Consumer Affairs
Agency, where one can search for a list of products for which manufacturers and others have reported their functional labeling. The
notification form allows for various details to be entered; hence,the search results output various information, including the functionality of the product. The functionality is described in natural
language, and the content is left to the discretion of the notifier, often resulting in content resembling product advertising. Unlike the
NARO database, as of 2023, more than 7,000 entries are registered.
However, it’s challenging to obtain data in a format that correlates
specific ingredients with their functionalities. In this study, we onlyuse data where both the functional ingredient, which is a compound,and its functionality can be identified.
4.1.4 FlavorDB. FlavorDB is a vast database concerning food andthe compounds contained within them. While there are other databases
detailing the relationships between food and compounds, FlavorDB
stands out for its extensive scale and its user-friendly provision ofthese relationships in a JSON format. Because of these advantages,
we adopted it in this study to combine ingredients, functionality,
and recipes. FlavorDB has a unique background, having been constructed for research and aggregation related to compound flavors.
Consequently, many of the compounds recorded in the database
are accompanied by word data related to flavors.
7 https://nousanbutsu-kinou.rad.naro.go.jp/
8 https://www.fld.caa.go.jp/caaks/cssc01/

IJCKG ’23, December 08–09, 2023, Tokyo, Japan

4.2

KG Construction Method

Ingredients and nutritional components data are derived from theRecipe1M+ structured dataset, while information concerning compounds contained in ingredients and functionality is sourced from
FlavorDB and various functional component databases aggregatedfrom the web. While it’s assumed that nutritional components likecarbohydrates, fats, proteins, and salt can be constituted by individual compounds found in ingredients, the specific amounts ofthese compounds are not included in the data retrieved from FlavorDB. Therefore, representing nutritional components with thecompound class was deemed challenging, leading us to treat themas separate classes.
Examples for linking data sourced from various datasets is shownin Table 2. In the table, Data source 1 and 2 respectively indicate
the datasets from which the instances for matching are obtained,
while Instance 1 and 2 provide examples of each instance.
Ingredients to Compounds: Matching is done based on thefood name. While Recipe1M+ ingredients may include information about their processing state, investigating differences
in individual compound content was deemed challenging.
Therefore, we matched with the food labels on FlavorDB.
Compounds to Functionality: Matching is conducted using
PubChemID. While compound data in FlavorDB is taggedwith PubChem IDs, many of the data on functional components either have IDs from the Japanese Chemical Substances
Dictionary or are merely denoted by their compound names.
We manually converted these to PubChemIDs. Due to this,even if the functional ingredients are identical to compounds
on FlavorDB, there is a possibility they might differ, such as
being different isotopes; thus, a perfect match is not guaranteed. In cases that require adjustments, it’s necessary toindividually review foundational documents, such as systematic reviews.
Ingredients to Functionality: For certain functionalities orcompounds that did not match, we matched based on ingredients containing that function. Ingredient data on FlavorDB islinked to the corresponding species’ Wikipedia page. Using
this, we retrieved the Japanese label for the relevant concept on Wikidata and matched it with the food labels in thefunctionality dataset. This approach might introduce somemismatches through incorrect pages. After investigating theaccuracy of this matching, it was found to be 92.1%.
Functionality to Functionality: All functionalities are represented in Japanese text. We simplified and consolidated
their representation based on the National Agriculture andFood Research Organization’s functional component database. Functionalities retrieved from the functional productsearch service were rewritten into a simpler expression. If
during this process, the content words perfectly matchedthose in the functional component database, or if there weresimilar expressions for the same compound’s functionality,
they were deemed to be the same functionality.

5

PROBABILISTIC LOGIC PROGRAMMING

Inference was implemented using the ’Learning from interpretations’ algorithm in ProbLog, a probabilistic logic programming

Kobayashi et al.

language. ProbLog is an extended version of a logic programming
language that allows assigning probabilities to each logical expression in the source code. Not only can it be described in notation
compliant with the Prolog logic programming language, but it also
possesses extended functionalities that cater to various inference
algorithms. Among the major lifestyle diseases are cancer, heartdisease, cerebrovascular disease, diabetes, hypertension, obesity,and fatty liver. For the recommended recipes, we assume the user’s
dinner and set an upper limit on the nutritional components ofthe meal to be recommended based on the nutrients the user has
consumed throughout the day. In particular, obesity is a significant
factor inducing other lifestyle diseases and serves as a barometer
indicating issues with one’s lifestyle balance. Therefore, using thepreviously mentioned knowledge graph and the user data discussed
later, we devised a method in ProbLog to recommend recipes effective for improving obesity. Criteria for determining obesity arepresented in 3. Additionally, physical examination items that may
indicate the possibility of obesity, might induce obesity, or may
worsen due to obesity are listed in 4.
Furthermore, excessive intake of nutrients, primarily sugars andfats, is cited as the main factor for obesity. Considering these points,
the following indicators for recipe recommendation were set, andprobabilities for each were determined from the training data:
is_obesity An indicator to determine if a user is obese. It isconsidered true if the conditions in table 3 are met.
intake_limit An indicator of the upper limit of various nutrients that the user can consume. Assuming dinner, it isdetermined by the difference between the amount of nutrients the user has consumed until dinner and the daily intake
limit.
blood_pressure An indicator to determine if the user has highblood pressure.
urine_protein An indicator of whether protein is present in
the user’s urine. Protein in urine is more likely to appear
due to decreased kidney function, and in cases of obesity,
kidney function is more likely to decrease. Hence, it serves
as a barometer for the risk of complications due to obesity.
blood_ldl Whether LDL cholesterol exceeds the threshold.
effective_function Whether the recommended recipe contains functional components effective against obesity.
The condition related to stress was removed to reduce computational complexity. A ProbLog snippet that combines these indicators
to show recipe recommendations is presented in Figure 3.
As illustrated in the figure, the decision to recommend a recipe
is based on the following three criteria: Nutrient Intake Limit Criterion: From the user’s dietary history for the day, this criterion
checks if the nutritional components of the recipe exceed the permissible intake limits. This criterion is represented in the figure by
the ’nutrition_limit’ equation. Obesity-Related Criterion: This evaluates whether the user meets any of the criteria indicating obesity
or any related measures. This criterion is represented in the figure
by the ’obesity_related_problem’ equation. Functional Criterion:
This checks if the ingredients or compounds in the recipe havefunctional properties effective against obesity. Represented by the’has_function’ equation in the figure, it retrieves the functional ingredients included in the recipe by exploring the knowledge graph.

Recipe Recommendation for Health Care Using Food Functionality Knowledge Graph and Probabilistic Logic Programming

IJCKG ’23, December 08–09, 2023, Tokyo, Japan

Table 2: Data Linking Examples.
Instance Type

Data Sources

Instances

Linking Method

Ingredient

Recipe1M+
FlavorDB"yogurt, greek, plain, nonfat""Yogurt"Word matching.

Ingredient

FlavorDB
Functionality Dataset"Cabbage""Cabbage (in japanese)"

CompoundFlavorDB
Functionality Dataset"Epigallocatechin Gallate""Epigallocatechin Gallate (in japanese)"

Pubchem ID matching.

Functionality

NARO Functionality DB
Customer Affairs Agency’s DB"Decrease high blood pressure.""... peptide decrease high blood pressure ..."

Manually matching.

Table 3: Criteria for determining obesity.
Indicator

Criteria

BMI
Body Fat Rate
Waist Size

25
Male:>20%, Female:>30%
Male:>85cm, Female:>90cm

Match through Wikidata item.

urine tests, the presence of physical and mental stress, and theaverage values of nutrients consumed per day over several days.
Regarding the consumed nutrients, since the recommendation isintended for dinner recipes, we used two-thirds of the values forthe calculations. We created 1,000 individual user data.

6

RECIPE RECOMMENDATION EXPERIMENT

Using the constructed knowledge graph and user data, we conTable 4: Criteria for obesity related diseases.
ducted experiments to determine the probabilities of the recommendation formula shown in Figure 3 and various obesity-related
indicators. To prepare the training data, we assigned real-number
Indicator
Criteria
Details
recommendation scores to recipes from Recipe1M+. These scores
Urine Protein
Positive
Possible nephropathy.
are automatically assigned based on the permissible nutritional
High Blood Pressure > 140/90 mmHg
Causes some deseases.
intake and the functionality of the food. If the score is positive, itHigh LDL Cholesterol> 140mg/dl
Causes of arteriosclerosis.
is recommended, and if it is negative, it is not recommended. The
High StressStress Check
Causes of overeating.
annotated data was created based on this criterion.
These scores are calculated so that recipes containing functional
ingredients have a higher score. While it is believed that this method
This criterion is realized by combining the proposed knowledge
could significantly influence the experiment results, this approachgraph with probabilistic logic inference. By attributing probabilities
was taken as a preliminary measure, anticipating the future creation
to each nutritional component in the ’nutrition_limit’ equation,of training data on whether each recipe should be recommended to
it is anticipated that we can identify which nutrients are criticalusers with the cooperation of registered dietitians.
(i.e., typically overlooked by the most of users). Moreover, by asFor the training data, five recipes recommended for each user andsigning probability values to each obesity-related measure in thefive recipes not recommended were randomly extracted from the’obesity_related_problem’ equation, we expect to infer which healthannotated data, constructing a total of 10,000 sets of recommended
issues are more prevalent among the entire user base from theseand non-recommended data pairs. In selecting these recipes, theprobabilities.
selection was narrowed down to 200 recipes in order to facilitate
The daily intake limit is determined by the public health authoricomparisons among users.
ties of each country. In this study, we adopted the maximum intake
The probabilistic values learned for each rule using this training
level, classified as Body Level III, from the dietary reference intakes
data are presented in Table 5. Training was conducted individuallydata published by Japan’s Ministry of Health, Labour and Welfare
for each user, and the table shows the average (𝑃 (𝐴𝑣𝑔.)) and stanas the intake limit[6]. As this intake limit varies depending on thedard deviation (𝑃 (𝑆𝑡𝑑.)) of the probabilistic values for each derived
user’s age and gender, each condition was individually incorporated
fact across users.
into the program.
Despite the variation in dietary history among users, it is evident
that the intake of energy, salt, and protein is on the higher side,
5.1 User Datasuggesting that most users tend to overconsume these nutrients.
In order to carry out the recommendations related to obesity, weOn the other hand, fats and sugars, which are significantly correcreated the necessary user data. The user data was constructed
lated with obesity, show a relatively low probability, indicating thatto correspond to the obesity-related indicators mentioned above,
they are not overconsumed. The results seem to reflect the typicalconsisting of general physical measurement data such as height,
nutritional distribution trends of Japanese cuisine.
weight, waist circumference, blood pressure, body fat percentage,
From the average and standard deviation of the ’obesity related
and gender. In addition, we also included data from blood tests,
problem’, it is evident that the variability regarding obesity-related

IJCKG ’23, December 08–09, 2023, Tokyo, Japan% Recommendation Rule For Obesity Persont(_)::recommend_food(User,Recipe):obesity_related_problem(User),\+ over_nutrition_limit(User,Recipe),
has_function(Recipe).
recipe(Recipe),user(User).
% For Obesity Person (No Function But Recommend)t(_)::recommend_food(User,Recipe):obesity_related_problem(User),\+ over_nutrition_limit(User,Recipe),\+ has_function(Recipe).
recipe(Recipe),user(User).
% For Healthy Persont(_)::recommend_food(User, Recipe):\+ obesity_related_problem(User),\+ nutrition_limit(User,Recipe),
recipe(Recipe),user(User).
t(_)::over_nutrition_limit(User,Recipe):is_over_limit_energy(User,Recipe);
is_over_limit_salt(User,Recipe);
is_over_limit_fat(User,Recipe);
is_over_limit_protein(User,Recipe);
is_over_limit_sugar(User,Recipe).
t(_)::obesity_related_problem(User):is_obesity_bmi(User); is_obesity_waist(User);is_obesity_bfat(User); is_urine_pro(User);is_blood_high_cholesterol(User);is_high_blood_pressure(User).
has_function(Recipe):has_edge_recipe_ingr(Recipe,Ingredient),(
has_edge_ingr_func(Ingredient, Function);(has_edge_ingr_comp(Ingredient, Compound),has_edge_comp_func(Compound, Function),compound(Compound))),ingredient(Ingredient),function(Function).

Figure 3: The ProbLog Snipet of Recipe Recommendation
Indicators.

issues is larger compared to other metrics. Moreover, it indicatesthat nutritional factors are given greater emphasis than whether
or not an individual experiences obesity-related concerns whenrecommending recipes. On the other hand, the proportion of participants exceeding the standard values for obesity is 54%, which isroughly half, and there isn’t a significant deviation from the average,
suggesting the scoring seems appropriate. Additionally, concerning
the obesity standard values, while 54% of the participants exceed
the body fat percentage standard, the proportion exceeding the
BMI and waist standards are lower (18% and 21%, respectively).
This indicates that there are many participants who might havesarcopenic obesity, a condition that has been on the rise in recent
years, as referenced in [18].

Kobayashi et al.

For the recommendation probabilities, the scores for recipes containing functional ingredients are higher than those without such
fact, indicating that recipes with functional components are more
likely to be recommended. On the other hand, recommendation
formulas targeting non-obese users also scored high, consistent
with the observed trend that whether or not a user is obese isn’t
given significant importance.
Using the results of this learning, we experimented to determine
whether it is possible to recommend recipes tailored to individuals. We picked up 25 recipes not used in the training data as test
data and selected three obese and three non-obese users to makerecommendations. As an evaluation metric, we calculated the rankcorrelation between the probability value when recommended andthe original score. The results are shown in Table 6. In the table, 𝜌
is a rank correlation score. 𝑃 (𝑁𝑢𝑡) represents the probability value
for the fact when excessive intake of any nutrient was observed,
𝑃 (𝑂𝑏𝑒) represents the probability value assigned for the fact when
there is suspicion of obesity, and 𝐹𝑢𝑛𝑐𝑃𝑟𝑖𝑜𝑟𝑖𝑡𝑦 indicates whether
the recommendation probability for recipes containing functional
components is higher than for those not containing them. All these
are related indicators as to which health improvement metric waseffective in recipe recommendation, which is the objective of thisstudy.
From the results, in terms of rank correlation, although the original correct annotation scores and the recommendation rules havea similar philosophy (e.g., scores increase when nutrients are notexceeded or when functional ingredients are included), there wasalmost no observable correlation. One possible reason for this discrepancy could be that while the recommendation rules determine
nutrient excess based solely on threshold values, the score calculation side affects the score based on the degree of exceedance. This
could mean that the recommendation system might not have beenable to adapt to the changes in ranking due to this influence.
For nutrient excess, the scores generally trended lower, similar tothe probability values during training. Particularly, users with lowscores (o1, o3, h2) had already exceeded the salt intake limit before
the recipe recommendation, suggesting that no matter which recipe
was recommended, they would deviate from this condition. As aresult, this rule seems to have been less considered.
In practical applications, even if a user has already consumed
too much of a nutrient, there is still a need to suggest recipes, so
this learning outcome is deemed appropriate. However, as mentioned earlier, there is a lack of metrics to evaluate the degree ofnutrient deviation. Therefore, there’s a possibility that recipes thatare not very appropriate in terms of nutrient balance might berecommended, suggesting a need to refine the rules.
The conditions regarding obesity showed a difference in scores
between users who are obese and those who aren’t, indicating that
it is given more importance for users who are obese.
For the rule of functional ingredients, the trend was consistent
with the results during training, showing that recipes containing
these ingredients were given more importance than those withoutthem.

Recipe Recommendation for Health Care Using Food Functionality Knowledge Graph and Probabilistic Logic Programming

Table 5: Statics of Learned Probabilities of Formularformular

𝑃 (𝐴𝑣𝑔.)𝑃 (𝑆𝑡𝑑.)recommend_food( Obesity & Function)
recommend_food(Obesity)
recommend_food(Healty)

0.711
0.504
0.717

0.187
0.235
0.177

nutrition_limit
is_over_limit_energy
is_over_limit_sugar
is_over_limit_salt
is_over_limit_protein
is_over_limit_fat

0.267
0.811
0.429
0.851
0.898
0.769

0.181
0.117
0.160
0.155
0.122
0.128

obesity_related_problem

0.502

0.363

Table 6: Recommendation Result

7

IJCKG ’23, December 08–09, 2023, Tokyo, JapanFor the recipe recommendation system, we defined metrics related to obesity improvement and determined their application
probability using the knowledge recorded in this knowledge graph.
Additionally, we conducted experiments to determine the probability of recommending recipes based on these probability values. As
a result, we could adjust the degree of application of criteria, suchas the presence or absence of functional ingredients and the excess
or deficiency of nutrients, depending on the user’s condition, andtrain the system to make appropriate recommendations. However,
we found that if the user has already exceeded the intake limit for
certain nutrients, there’s a possibility that a suitable recipe might
not be recommended. In the future, we are considering expanding
the rules to cover other lifestyle diseases besides obesity and arelooking into improvements that allow for more flexible condition
settings.

ACKNOWLEDGMENTS

userID

Obesity𝜌

𝑃 (𝑁𝑢𝑡)𝑃 (𝑂𝑏𝑒)𝐹𝑢𝑛𝑐𝑃𝑟𝑖𝑜𝑟𝑖𝑡𝑦

o1
o2
o3
h1
h2
h3

True
True
True
False
False
False

0.025
-0.039-0.007-0.004-0.016
0.056

0.149
0.497
0.347
0.556
0.058
0.300

0.234
1.0
0.714
9.13𝑒 −6
0.009
0.297

False
True
True
True
False
True

CONCLUSION AND FUTURE WORK

We proposed a system for recommending recipes based on indicators leading to the improvement of lifestyle-related diseases, such as
recipes containing functional ingredients. This system combines aknowledge graph that includes data on recipes and functional ingredients with probabilistic logical reasoning. The knowledge graphis composed of the large-scale recipe dataset Recipe1M+, the functional ingredient database provided by the National Agriculture andFood Research Organization, and the functional ingredient labelingfood notification database provided by Japan’s Consumer Affairs
Agency. Such a knowledge graph, which comprehensively records
functional ingredients, has not existed before and is considered to
be a highly valuable database. On the other hand, it became clearthat the data related to functional ingredients could be much better
compared to the recipe data obtained from Recipe1M+. Moreover,there needs to be more data related to ingredients, suggesting a needto proceed with data acquisition from external ontologies such as
FoodOn. As for functional ingredients, some compound information
included in Chebi[9] has features corresponding to food functions,so in the future, we aim to enhance functional information from
Chebi. Additionally, as data concerning functionalities and compounds becomes more available, we are considering implementing
inferences using probabilistic logical inference programs with deeplearning[12, 19, 22], and characterizing each node on the knowledge graph using GraphEmbedding[4], similar to FlavorGraph[16].
Many of FlavorDB’s compounds are annotated with flavor information related to the tastiness of food. By adding this information,
along with preferences from subject data, we are also exploring
recommendations based on taste and food preferences.

This work was supported by the Cross-ministerial Strategic Innovation Promotion Program (SIP) II- "Technologies for Smart Bioindustry and Agriculture" and III- "Building a sustainable food chainthat provides abundant and nutritious food (tentative)" funded by
the Bio-oriented Technology Research Advancement Institution.

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Received 18 September 2023; revised 30 October 2023; accepted 21 October
2023