# Application of an ontology-based decision support system for the design of emulsion-based cosmetic products

Juliana Serna, Jose Luis Rivera-Gil, Alex Gabriel, Javier Arrieta-Escobar, Vincent Boly, Véronique Falk, Paulo C. Narváez Rincón

► To cite this version:

Juliana Serna, Jose Luis Rivera-Gil, Alex Gabriel, Javier Arrieta-Escobar, Vincent Boly, et al. Application of an ontology-based decision support system for the design of emulsion-based cosmetic products. 32nd European Symposium on Computer Aided Process Engineering, Jun 2022, Toulouse, France. pp.847-852, 10.1016/B978-0-323-95879-0.50142-9. hal-04673718

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# Application of an ontology-based decision support system for the design of emulsion-based cosmetic products

Juliana Serna<sup>a,b</sup>, Jose L. Rivera-Gil<sup>a,b</sup>, Alex Gabriel<sup>a</sup>, Javier A. Arrieta-Escobar<sup>b,c</sup>, Vincent Boly<sup>a</sup>, Véronique Falk<sup>c</sup>, Paulo C. Narváez-Rincón<sup>b</sup>

## Abstract

The decision-making process for the design of formulated products faces different challenges because of its intrinsic complexity. On the one hand, it is not sequential, but iterative due to the fragmented and heterogeneous nature of available information. On the other hand, there is not a unique design workflow because it changes from company to company according to its context and specific requirements. The lack of structure of knowledge for product formulation requires developing a robust knowledge representation to show coherently and explicitly concepts, models, and data. Furthermore, this representation must allow design teams to use it flexibly and to adapt it to specific design contexts. In view of the above, this work proposes an ontology for formulated products with emphasis on cosmetic emulsions. This ontology integrates concepts from emulsion science, cosmetic formulation, expert knowledge, and design heuristics in a systematic and accessible way. It was done based on the recent work of our research group in Chemical Product Design. This document shows an overview of the ontology and one of its possible applications: verification of the formulation of a skin care cream. As a conclusion, it was found that the ontology enables the access to precise information according to design requirements. It is a versatile and useful information tool for the design of emulsion-based products.

**Keywords:** Product design, Cosmetic emulsions, Ontology

## 1. Introduction

Formulated chemical products, such as cosmetics, are complex systems whose properties are defined by a synergistic action of ingredients, composition, and production process. Their design is a scientific and organizational challenge, as it requires the management of heterogeneous information from multiple sources and the application of multidisciplinary knowledge (Zhang et al., 2020). Available information for their design is huge and diverse in nature: there are multiple ingredient databases, property models, heuristics, knowledge from colloidal science, regulations, recommendations, guidelines, etc. Additionally, in the field of Chemical Product Design (CPD), there are several methodologies and computer-aided methods. Examples are as a design methodology for cosmetic formulations incorporating heuristics (Arrieta-Escobar et al., 2020), a methodology for formulated products applying expert knowledge (Serna et al., 2021), and an integrated framework for formulated products including liquids, solids, emulsions (Zhang et al., 2017), among others.

<footnote><sup>a</sup>Université de Lorraine, Equipe de Recherche des Processus Innovatifs, ERPI-ENSGSI, 8 Rue Bastien-Lepage, 54000 Nancy, France</footnote>

<footnote><sup>b</sup>Universidad Nacional de Colombia, Departamento de Ingeniería Química y Ambiental, Grupo de Procesos Químicos y Bioquímicos, 111321 Bogotá, Colombia</footnote>

<footnote><sup>c</sup>Université de Lorraine, Laboratoire Réactions et Génie des Procédés, CNRS-LRGP-ENSIC, 1 Rue Grandville, 54000 Nancy, France
jsernaro@unal.edu.co</footnote>
Despite its abundance, the application of the existing information for product formulation of is not straight-forward (Zhang et al., 2020). Design methodologies are not adapted to practical design processes, which are iterative and change according to the design context. Additionally, available information is not centralized, has different forms (qualitative and quantitative), and it is frequently fragmented. Thus, it is necessary to create tools capable of managing real design contexts and available information to solve practical problems. In view of the above, this work proposes OntoCosmetic, an ontology to systematically represent knowledge for the formulation of cosmetic products of the type of homogenous mixture or emulsion. It is proposed to support data storage and analysis, information modeling and decision-making for cosmetic formulation. It is based on the knowledge and information gathered and developed by our design team in CPD (Arrieta-Escobar et al., 2020) (Serna et al., 2021) and it has a structure that can be extended to represent more general information in the field of formulated chemical products. The ontology was developed following the ontology creation methodology described by Gabriel (2019) and formalized using the software Protégé<fnref n="1" />. In this document, section 2 presents a short introduction to ontologies and an overview of OntoCosmetic. Section 3 presents an application of the ontology for the verification of a formulation in relation to a heuristic database. Finally, Section 4 highlights some conclusions and perspectives.

## 2. An ontology for cosmetic product design

### 2.1. Concept of ontology

Ontology is “an explicit specification of a conceptualization” which is an abstract, simplified view of the world that is represented for some purpose (Gruber, 1993). It is holistic since it contents all relevant concepts and relationships between concepts to model a specific subject or phenomenon (Hailemariam & Venkatasubramanian, 2010). It is also as precise as required because it is normally defined within a specific domain of knowledge and with a clear purpose (Gabriel et al., 2019). In software engineering, ontologies are seen as “a means to efficiently build the knowledge-based necessary to effectively support engineering work processes” (Marquardt et al., 2010).

The main elements of an ontology are: *Classes* which are concepts or sets containing instances (e.g. the class ‘Ingredient’ in a cosmetic formulation). *Relations* which show the relation between classes (e.g., the relation ‘has’, connecting ‘Formulation’ with ‘Ingredient’). *Instances* which are the elements of the classes (e.g., ‘Argan oil’, which belongs to the class ‘Ingredient’). *Rules* which are logical statements that can be used to make inference (e.g., heuristics).

### 2.2. An ontology for cosmetic product formulation

OntoCosmetic was constructed based on the ontology OntoCAPE (Marquardt et al., 2010), a very elaborated ontology for process design in the field of Chemical Engineering. In OntoCAPE, products and processes are technical systems, which are developed in an engineering design process to meet defined requirements (Marquardt et al., 2010). There, technical systems are represented through five viewpoints: requirements, function, realization, behavior, and performance. OntoCosmetic uses part of this description to define the Product System and adapts it to the application of cosmetic formulation.

Figure 1 shows an overview of OntoCosmetic. It is organized in two interconnected blocks: A) Product System, containing all the relevant concepts describing a cosmetic formulation, B) Product Model, containing a knowledge base of models to support practical decision-making during the formulation process.

<footnote n="1">¹ http://protege.stanford.edu/</footnote>
<figure><img src="image_2.png" /><figcaption>Figure1. Overview of the OntoCosmetic ontology. A) Product System B) Product Model. The dark area corresponds to the concepts adapted from OntoCAPE</figcaption></figure>

In OntoCosmetic, Product System is divided in four viewpoints:

* Product requirements: It corresponds to the product desired behavior, represented by product specifications and target values. It is a 'to be' relation.

* Product materialization: It corresponds to the decisions that designers can make and implement to achieve product requirements. For example, the selection of ingredients. Product materialization has the modules of process and substances. The latter is a super class of the ingredient class which is connected to a database of cosmetic ingredients.

* Product behavior: It corresponds to the real behavior of the product whether it is expected or not. It can be characterized quantitatively, through product properties, or qualitatively, through a qualitative product description. Product behavior can also be related to a phenomenon, to the micro-structure of the product (in the case of emulsions, microstructure is frequently characterized by particle size and particle size distribution), and to the phase system (which describes the product as a thermodynamic system).

* Product performance: It corresponds to the evaluation of the Product System specifically in relation to product requirements. It indicates whether the product has the desired performance (as described by product requirements) or not. The evaluation is done through target value indicators and sustainability indicators. In this case, in contrast to product requirements, indicators are related to the product system with a 'as is' relation.

The ontology has been developed with focus on cosmetic applications. Thus, it contains concepts and databases specifically related to this subject. In the ontology, ingredients are classified in subclasses that enable their search based on cosmetic related categories. Figure 2 shows some sub-classes of the class Ingredient with their definitions. Figure 3 shows some attributes of the sub-class Emollient with their definitions.
<figure><img src="image_3.png" /><figcaption>Figure 2. Extract of Ingredient sub-classes in OntoCosmetic</figcaption></figure>

<table><caption>Table 1. Extract of Ingredient sub-classes in OntoCosmetic</caption><thead><tr><th>Function</th><th>Description</th></tr></thead><tbody><tr><td>Ingredient</td><td>Chemical compounds that are part of a formulation</td></tr><tr><td>Emollient</td><td>Oils used in cosmetic emulsions for several purposes: to make skin and hair softener, to serve as solvents for liposoluble actives. Some of them can act as occlusive ingredients.</td></tr><tr><td>Surfactant</td><td>Ingredient used in emulsions for three purposes: to decrease the energy required during emulsification, to adsorb rapidly at the interface of recently formed droplets, and to generate a barrier for long-term emulsion stability.</td></tr></tbody></table>

By its part, Product Model contains the models and tools that enable designers to make decisions. Product models are classified in the following classes:

* Heuristic: It is a guideline or rule of thumb expressed in natural language that gives a practical instruction to achieve a product requirement. It can be transformed into an algebraical mathematical model.

* Expert knowledge: It is a qualitative relation between the classes of product behavior and product materialization. They were established with the help of experts in emulsion science. They are used in combination with emulsion science principles.

* Mathematical model: It is an equation that enables a quantitative definition of ingredients in relation to product requirements.

* Emulsion science principles: They are scientific descriptions of emulsion phenomena, which explain some product system behaviors and can be used for decision-making in combination with expert knowledge.

<figure><img src="image_4.png" /><figcaption>Figure 3. Some attributes of the class emollient</figcaption></figure>

<table><caption>Table 2. Some attributes of the class emollient</caption><thead><tr><th>Function</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>requiredHLB</td><td>Real</td><td>It determines the compatibility of oils with the surfactant system</td></tr><tr><td>greasiness</td><td>String</td><td>It that reflects the oiliness of the skin after a lotion is applied. Allowed values: {Very high, High, Medium, Low}</td></tr><tr><td>viscosity</td><td>Real</td><td>It is the measure of the resistance of a fluid to deformation.</td></tr><tr><td>origin</td><td>String</td><td>Ingredient source. Allowed values: {natural, from nature raw materials, from natural and synthetic raw materials, synthetic}</td></tr></tbody></table>

As well, Product models are classified into sub-classes according to their applicability (general emulsions, cosmetic emulsions, specific cosmetic applications). Additionally, their implementation is related to the product materialization system (a model can be related to one or more ingredient class, sub-class, or attribute), and their effect is related to the classes of product behavior and product performance systems. Examples of product models and their relations are presented in Table 3.
<table><caption>Table 3. Some product models and their relations</caption><thead><tr><th>Product model</th><th>Description</th><th>Application</th><th>Relations</th><th>System</th><th>Range</th></tr></thead><tbody><tr><td>Heuristic</td><td>For a good sensorial, at least one emollient of each spreading type should be used: high, medium, and low.</td><td>Cosmetic emulsion</td><td>isImplementedBy<br>hasEffectOn</td><td>Product Materialization<br>Product Performance</td><td>Emollient; Spreading<br>Performance indicator; Sensorial</td></tr><tr><td rowspan="2">Emulsion Science</td><td rowspan="2">Emulsion stability is affected by the phenomenon gravitational separation.</td><td>General</td><td>hasEffectOn</td><td>Product Performance</td><td>Performance indicator; Stability</td></tr><tr><td>General</td><td>isRelatedtoPhenomenon</td><td>Product Behavior</td><td>PhenomenonGravitationalSeparation</td></tr><tr><td rowspan="2">Emulsion Science</td><td rowspan="2">Gravitational separation can be controlled with the addition of a thickener.</td><td>General</td><td>isImplementedBy</td><td>Product Materialization</td><td>Thickener; Concentration</td></tr><tr><td>General</td><td>isRelatedtoPhenomenon</td><td>Product Behavior</td><td>PhenomenonGravitationalSeparation</td></tr></tbody></table>

### 3. Application: Checking if a given formulation follows the heuristics

The Ontology was used to verify if a given cosmetic formulation follows or not a group of heuristics, and thus if it is recommended based on them. The verification was done using the Semantic Web Rule Language (SWLR) in Protégé, where heuristics can be written as logic rules involving instances of classes. The formulation checked against the heuristics is shown in Table 4 and the considered heuristics are presented in Table 5. As result, the reasoner classified the formulation as recommended according to selected heuristics (ConformFormulation).

<table><caption>Table 4. Formulation checked against some heuristics</caption><thead><tr><th>Class</th><th>Ingredient</th><th>[%]</th></tr></thead><tbody><tr><td>Emollient</td><td>Ethylhexyl Palmitate</td><td>4</td></tr><tr><td>Emollient</td><td>Olive oil</td><td>3</td></tr><tr><td>Emollient</td><td>Caprylic/Capric Triglyceride</td><td>9</td></tr><tr><td>Surfactant</td><td>Glyceryl Stearate Citrate</td><td>2.5</td></tr><tr><td>Surfactant</td><td>Oleth-3</td><td>0.5</td></tr><tr><td>Surfactant</td><td>Sorbitan Monostearate</td><td>1</td></tr><tr><td>Preservative</td><td>Cosgard</td><td>0.7</td></tr><tr><td>Antioxidant</td><td>Tocopherol</td><td>0.5</td></tr><tr><td>Humectant</td><td>Glycerin</td><td>3</td></tr><tr><td>Thickener</td><td>Xanthan gum</td><td>0.1</td></tr><tr><td></td><td>Water</td><td>To 100</td></tr></tbody></table>

<table><caption>Table 5. Heuristics considered to test the formulation using the ontology</caption><thead><tr><th>Product model</th><th>Description</th><th>System</th><th>Range</th></tr></thead><tbody><tr><td>Heuristic</td><td>at least one emollient of each spreading type should be used: high, medium, and low</td><td>Product Materialization</td><td>Emollient; Spreading</td></tr><tr><td>Heuristic</td><td>Use a minimum level of 2% and up to 5% of surfactants</td><td>Product Materialization</td><td>Surfactant; SurfactantComposition</td></tr><tr><td>Heuristic</td><td>At least one ingredient should be of the natural origin</td><td>Product Materialization</td><td>Ingredient; Origin</td></tr><tr><td>Heuristic</td><td>surfactant/emollient ratio should be between 1:4 to 1:6</td><td>Product Materialization</td><td>SurfactantComposition; EmollientComposition</td></tr><tr><td>Heuristic</td><td>HLB should match the RHLB required</td><td>Product Materialization</td><td>Surfactant; Emollient; SurfactantComposition; EmollientComposition</td></tr></tbody></table>

As shown in Figure 4, the formulation is introduced to the system as an instance with relations to ingredients (composition) and rules (heuristics that the formulation should
follow). Based on this information, the reasoner activates the required rules and classifies
the formulation as ConformFormulation, when it follows the rules or as
NonConformFormulation, when it does not. The formulation must be experimentally
validated to verify that it meets the requirements.

<figure><img src="image_5.png" /><figcaption>Figure 4. Results of the verification in Protégé</figcaption></figure>

4. Conclusions and perspectives

The article presents a brief overview of OntoCosmetic, an ontology for cosmetic product design. Currently, the ontology formalizes concepts of product formulation. It can be used to create information systems with common definitions, to call ingredient and formulation information through a SPARQL<fnref n="2" /> request, to apply heuristics through the activation of rules. The perspective of this ontology is to create an application programmer interface (API) to allow the integration of knowledge into a software. This software would assist the decision-making process during design as well as visualize data and optimize the formulation of cosmetic products considering multiple criteria.

References

Arrieta-Escobar, J. A., Camargo, M., Morel, L., Bernardo, F., Orjuela, A., & Wendling, L. (2020). Methodology for the Design of Formulated Products Incorporating Consumer Preferences and Heuristic Knowledge. https://doi.org/10.22541/au.158879155.54817449

Gabriel, A., Chavez, B. P., & Davy, M. (2019). Methodology to design ontologies from organizational models: Application to creativity workshops. Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, 33(2), 148–159. https://doi.org/10.1017/S0890060419000088

Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5, 199–220. https://doi.org/10.1006/knac.1993.1008

Hailemariam, L., & Venkatasubramanian, V. (2010). Purdue ontology for pharmaceutical engineering: Part I. Conceptual framework. Journal of Pharmaceutical Innovation, 5(3), 88–99. https://doi.org/10.1007/s12247-010-9081-3

Marquardt, W., Morbach, J., Wiesner, A., & Yang, A. (2010). OntoCAPE A Re-usable Ontology for Chemical Process Engineering. In RWTHedition. Springer.

Serna, J., Narváez Rincón, P. C., Falk, V., Boly, V., & Camargo, M. (2021). A Methodology for Emulsion Design Based on Emulsion Science and Expert Knowledge. Part 1: Conceptual Approach. Industrial & Engineering Chemistry Research, 60(7), 3210–3227. https://doi.org/10.1021/acs.iecr.0c04942

Zhang, L., Fung, K. Y., Zhang, X., Fung, H. K., & Ng, K. M. (2017). An integrated framework for designing formulated products. Computers and Chemical Engineering, 107, 61–76. https://doi.org/10.1016/j.compchemeng.2017.05.014

Zhang, L., Mao, H., Liu, Q., & Gani, R. (2020). Chemical product design – recent advances and perspectives. Current Opinion in Chemical Engineering, 27(November 2019), 22–34. https://doi.org/10.1016/j.coche.2019.10.005

<footnote n="2">² SPARQL Protocol and Resource Description Framework (RDF) Query Language</footnote>