Ingredients ontology

Why?

Why do we need an ingredients ontology? The ontology describes how ingredients are derived from each other and how ingredients can be combined into new ingredients. An ontology might be useful to:

• Normalise ingredients - Producers take a lot of freedom in describing the ingredients they use. An ontology helps to standardise the ingredients.
• Hidden ingredients - an ingredient might contain hidden ingredients, the ontology might reveal these. For example butter contains butterfat.
• Combined ingredients - an ingredient might appear as a single ingredient. In reality however
• Processed ingredients - often an ingredient is derived from an other ingredient through some process. We can make explicit what these processes are. Example clarified butter is created from butter by separating the milk solids and water from the butterfat.
• Ingredient incompleteness - often an ingredient is incomplete defined in an ingredient list. For instance if an ingredient-list specifies milk, it should be defined from which mammal the milk comes from, for instance cow's milk.

Theory

What theory can be used to base an food ingredients taxonomy on? Is there already a food ontology somewhere?

OWL

I get the impression that OWL is the most accepted markup language to exchange semantic networks. I will use the Manchester markup to describe the axioms.

Tools

Discovering Protégé from Stanford at the moment. There are standalone versions and web-versions of the app. Maybe the web-version can be used to maintain the ontology by OFF-users.

Existing ontologies

It would be a pity if we have to develop ontologies for all the

Languages

Several language ontologies exist:

• Lingvoj seems to be a languages ontology, which is available in rdf. It is not clear how to download the individual language files.
• The EU has a XML and SKOS download (Language NAL).
• Lexvo (RDF-file can not be loaded by Protégé)

These solutions seem to be overkill for our needs.

Food

Can we use LinguaL in some way? Maybe for inspiration.

DBPedia

This should provide links to wikipedia, short summaries and be multilingual.

Geography

This is needed to get some structure for locations (useful for IGP).

Labels

Are there taxonomies/ontologies for labels?

Issues

It is not easy to convert all aspects of OFF to OWL and Protégé. Some issues that have been encountered:

• Translations
• Inheritance

Ontology building

This section describes how the ontology is created starting from the ingredients found on the ingredient lists of products.

Individuals

The purpose of the ontology is to have a set of axioms between canonical ingredients, which is logically consistent. If a producer puts an ingredient in an ingredient list (the raw ingredient), it will first be normalised to a single canonical ingredient name. This canonical name will define the ingredient class. These canonical ingredient names are the individuals in OWL/Protégé terminology.

Raw ingredients

The creation of the ontology starts with the ingredients as found on the ingredient lists. A raw ingredient is defined as the the text between two ingredient dividers (where are these defined?). This might include any percentage, organic markup, allergen markup, origin and sub-ingredients. In combination with the language used, it gives a first indication of the ingredient class.

Normalized ingredients

The raw ingredients must be transformed to normalized ingredients. A normalized ingredient is a single string in a western script (assumes that the western script can be read by any one in the world). A normalized ingredient is preceded by the language code of the ingredient. Usually this in english if the translation is available. (It is possible that the from language is needed for a better comprehension when classifying ingredients).

Normalizing ingredients implies that any variant of an an ingredient in any language is mapped onto a single name. The normalizing procedure is mainly going through a pattern recognition procedure.

For protégé this implies that any normalized ingredient individual must have a data property, which defines the data corresponding to the individual. The data property hasName valueString is used to infer the corresponding classes. It is assumed that the valueString has the format languageCode:string. This data property can then be used to classify an ingredient.

Probably it is possible to bypass this normalisation step, but that makes the encoding in OWL much more extended.

Normalisation procedure

• keep and show - try to keep the original entry as much as possible (remove typo's, etc) and show the user what the canonical variant is.

• plural / singular - should all ingredients be defined in singular or plural. It probably depends what is meant (or left out). If a raw ingredient says minerals or salts, it does not say which mineral or salt. There can be more than one mineral or salt. So in the normalisation plurals should be kept.

• split/combine - it is tempting to combine ingredient names that point to the same ingredient. Whether one should do so depends on practical usage of the names. If both the names are used as often, it is better to keep them separate. If one name is obscure, let it be a synonym of the more prevalent name. For instance butterfat and ghee are used as often and should be kept separate, although they are the same ingredient.

• ingredient/product - ingredient lists can contains products, or better compound ingredients. A compound ingredient consists of multiple sub-ingredients. This can be listed as: compound ingredient (subingredient 1, subingredient2,...) or compound ingredient: subingredient1, subingredient2, ...;. This structure should be kept intact, so a better normalisation can be done.

• translations - for each canonical ingredient-name, translations in other languages might exist. The best is to deduce the translations from the actual ingredient lists. However be careful, the producer might have taken some liberties with these translations. If you think the translation is faulty do not add it. Create two different canonical ingredients and have them point to each other.

• explanatory translations - it is tempting to add a description if the translation does not exist. Such a explanatory translation can be part of a class annotation.

Classes

The classes define the Ingredients, Languages and other concepts used in the ontology.

Ingredient Classes

The ingredient classes are the sets that represent the individuals. Each normalised individual will be mapped to a single class.

URI

Each class has a unique URI. We use the canonical ingredient name for this URI plus a namespace prefix, for example: OFF_Butter

Necessary conditions

Each ingredient class has a necessary and sufficient condition (equivalent class expression) consisting of a single data property (one can also specify multiple data properties here if one does not want to do the normalisation):

DataPropertyAssertion(  :hasName :Butter xsd:string[pattern ".*n:.Butter"])

Annotations

A class can further be described by annotations, such as:

• label - a user friendly name of the class, probably the canonical name of the ingredient with a corresponding language code. It is possible to add names for eah language. These labels can then serve as translation strings.
• wikipedia - a link to the corresponding ingredient on Wikipedia. Preferably this link is in english, but other language inks can be added as well
• wikidata - the link to the english wikidata entry.
• openFoodFacts ingredients - a link to the corresponding world ingredient page on OFF.
  • comment - an comment in english can be added to explain how the ingredient is defined.

Axioms

In addition one could associate one or more axioms that defines how the ingredient relates to other ingredients.

Class hierarchy

It possible to group ingredient classes into other parent classes. These parent classes are not defined by the hasName data properties. By looking at the common axioms of ingredient classes, it is possible to define a parent class.

For instance all butters share the fact that they have butterfat, that the percentage of butterfat is more than 80%, that they have been derived from cream (either by churning or fermenting), that there is still water in it, etc. It is even possible to define multiple parents, as long as the axioms are shared. An approach could be to decide on a defining ingredient to get the set of base axioms. Any other ingredient that has at least this base also belongs to the parent.

Language Classes

For each language a separate class can be defined. The name of the class, English Language for instance, is in english and makes clear what the class is about. The annotation data for the language can be taken from Lingvoj by hand. Lingvoj has each language defined as a separate individual. We want to assign each ingredient individual to a specific language class. Each language is a sibling of the superclass Language.

Each language class has a data property, which is necessary and sufficient condition that determines its membership. This data property describes how the language is encoded in an ingredient individual. This encoding consists of the languageCode that corresponds to the language. with the corresponding. For example for an ingredient individual in english:

DataPropertyAssertion(  :hasName :Butter xsd:string[pattern "en:.*"])

This uses a regex-string for decoding.

Origin classes

These classes define the original source of an ingredient, before any processing is done. For instance milk comes from female mammals, cow milk comes from female cows, salt comes from the mineral halite, etc.

The food thesaurus created by LanguaL can be used as a base

Label classes

These classes describe labels that have been assigned to Ingredients.

Geography Classes

These classes describe the geographic origin of classes. These can be added as necessity requires. A hierarchy based on continent and country or region can be added.

Object properties

The object properties define how the ingredients are related to each other. These object properties are defined independent of the classes (that is a base idea in OWL).

Axioms

Each object property is an axiom that can be applied to classes.

In addition one could one or more axioms that define the ingredient in relation to other ingredients. For instance:

• Component axioms - these axioms define the components of an ingredient. These component ingredients are probably mostly base ingredients that can nog longer be broken down. The axioms also have an inverse axiom.

ObjectPropertyAssertion( :contains :Butter :Butterfat )

• Derivation axioms - these axioms define how one ingredient has been transformed into another ingredient. Not sure if these (or all of these) have an inverse axiom.

ObjectPropertyAssertion( :isDerivedFrom :Butter :Cream )

• Proportion axioms - these axioms define how much of an component ingredient is present.

DataPropertyAssertion( :hasPercentageOfButterfat :Butter xsd:double[>= "80"^^xsd:double, <= "99"^^xsd:double] )

• Origin axioms - axioms that define where an ingredient is manufactured.

ObjectPropertyAssertion( :isManufacturedIn :Butter :Isigny )

• Label axioms - these axioms define if the ingredient has a label, such as AOP, Organic, etc.

ObjectPropertyAssertion( :isLabel :Butter :AOP )

Issues

It seems a lot of data is entered double. I hope these will solved as I understand things more.

Example

Maybe I can make a drawing of a part of the ontology.

Ontology Usage

The goal is to obtain an ontology that can be used by OFF to analyse the ingredients in an ingredient list. One could envisage the following steps:

1. The ingredient instance/entry/individual is entered into the inference engine
2. The engine infers a Ingredient Class and its subclasses
3. The results can be presented to the user and will explain what the class is, how it is related to other Ingredient Classes, how the Ingredient Class is created from other classes and it will show what the ingredient entry does not tell.
4. The user can select the language in which he wants to see the results.

OFF Ingredients Taxonomy

The ontology should be usable as the translations taxonomy. This taxonomy lists all ingredients, their synonyms and their translations. This taxonomy is already in use.

Object properties

The object properties describe the relations between classes.

The following object properties have been identified:

Contains/isPartOf

These properties allow to indicate whether an ingredient is composed of other ingredients. The relations are only defined if they clarify the definition of an ingredient class.

isDerived

This is a class of properties that describe how one ingredient class is transformed into another ingredient class.

isManufactureIn/manufactures

This describes the relation with a geographic place class.

Example

# CLARIFIED BUTTER - en:milk fat rendered from butter to separate the en:milk solids and water from the en:butterfat

<en:butter
en:clarified butter
bxr:Шара тоһон
ca:mantega clarificada
cs:přepuštěné máslo
de:Butterschmalz

Explanation

• The # describes a comment line and can be used to add a definition of the ingredient. In this case the definition is taken from wikipedia.
• The <en:butter line describes the parent ingredient of this ingredient. The parent ingredient forms the basis of the current ingredient. This line is optional. It implies that any properties of the parent are also valid for the child, unless a property has been redefined.
• The en:clarified butter line is the main name of the ingredient. Any synonyms appear after the main name, separated by comma's. The prefix en: defines the language of the main ingredient.
• The next lines provide translations of the main ingredient in other languages. One language per line. Each line starts with a language prefix. Thus de: means german.

Categories Taxonomy

OFF maintains a categories taxonomy to categorise products. This categories taxonomy is related to the ingredient ontology, but subtly different. First a product is something that is on sale and has an identifier (barcode). A product will never be part of an ingredients list. You will never see a barcode in the ingredients.

The relationship between product categories and ingredients can be described as: A product with a product name and barcode from a brand belongs to a category and has one or more ingredients.

For example the product Beurre Gastronomique Doux of the brand Milbona with barcode 20139315 has one ingredient: Beurre pasteurisé and belongs to the Sweet cream butters category.

Allergens taxonomy

OFF uses an allergens taxonomy/thesaurus to detect/describe allergens. We need to indicate the relation with this ontology.

Maintenance

This section described how the taxonomy file should be maintained, i.e. adding ingredients, editing ingredients, etc.

Automatic add

Describes how automatically new ingredients can be added from the ingredients available in the OFF-database. @stephane

Add single ingredient

An ingredient is a node in the taxonomy file, which describes a single ingredient as is found in an ingredient list.

Add ingredient name

You can add an ingredient anywhere in the file at first. Keep a blank line between the previous and next ingredient. Add the ingredient in the format: LANGUAGE_CODE:MAIN_INGREDIENT_NAME, SYNONYM, SYNONYM.

Thus start by defining the ingredient name you want to add. If there are multiple ingredients names possible, decide which ingredient name will be the MAIN_INGREDIENT_NAME. The other names can be added as SYNONYM. Each ingredient name is separated by comma's.

Determine the language code to be used. Find your language in this lang list and add the corresponding ISO 639-1 code as LANGUAGE_CODE to your MAIN_INGREDIENT_NAME, separated by a colon (:).

Add translations

You can add translations for an ingredient. Each translation should appear on a new line.

• Wikipedia translations

If the ingredient exists as article in Wikipedia, you can add the translations supplied by Wikipedia. You can use the language codes used by Wikipedia (as seen in the article url). You should use the title of the articles, as this corresponds the wikipedia link and might include some disambiguation.

Warning: you can not add the three (or more) letter codes used by Wikipedia. OFF does not support those. However you might add them as comment lines, so we have hem for the future.

• Other translations

It is possible to add other languages through an online dictionary. Linguee might help you here if you speak multiple languages. But preferable this should be checked by native speakers.

• Untranslatable ingredients

Sometimes an item can not be translated, as it just does not exist. If possible we could add a description instead.

Sort translations

The translations should be sorted in alphabetical order of the language code.

An exception is the first line, that is the default language, which is used, when no translation is present.

Add corresponding wikipedia entry

On a new line you can add the wikipedia article that corresponds the ingredient. The format to use for this line is:
Wikipedia:https://LANGUAGE_CODE.wikipedia.org/wiki/MAIN_INGREDIENT_NAME
So be careful that your main ingredient name is indeed a wikipedia article. This does not always work out as such.

Add a parent ingredient

It is possible to link ingredients based on related properties. If you can answer the question "The ingredient is a kind of other ingredient", then you can probably assign that ingredient as parent. First search if it exists already. If not then you should add the parent ingredient as well to the ontology. And add the parent ingredient in the format: <LANGUAGE_CODE:PARENT_INGREDIENT_NAME

Issues

Language codes

OFF seems to use the short ISO 639-1 language codes. The consequence is that not all languages that are found on Wikipedia can be implemented in OFF. For instance the language Furlan, does not have a ISO 639-1 code, but does have a ISO 639-2 code (fur).

Disambiguation

How should disambiguation be handled?

Product Data

The current taxonomy is based on the strings found in the ingredients lists. This does not include the percentages, nor the organic markup. In addition for a better class assignment, other data from the product is useful (nutritional values, production place).

Scalability

First experiments with classification of raw individual axioms, shows that the reasoner breaks down. This suggest that at least the reasoner is not very scalable. I hope the solution is.

Comments

Stephane @ 2018-08-22

For the ingredients taxonomy, I think it's best if we take a pragmatic and incremental approach:

We currently have 2 uses for the ingredients taxonomy:

1. Compute the % of unknown ingredients so that we can tag products that have ingredient lists that are likely bogus. -> this is currently not very useful as we have tens of thousands of products tagged

2. Compute the NOVA score for transformed products. -> this is in production today, and displayed on the web site and the mobile apps. -> there is an increasing attention on NOVA

It's the NOVA classification that has really made the ingredients taxonomy a reality: it's something that is visible, and we can incrementally improve the ingredients taxonomy to improve the classification. There is a 3rd use that I intend to develop soon:

3. Spellcheck and auto-correct the lists of ingredients. --> if we can make this work reasonably well, this will have a huge impact for OFF and help us to speed adding new products data and rely more on OCR. For those uses, we need:

- for 1 and 3, a list of correctly spelled ingredients, as comprehensive as possible

- for 2, a hierarchy of ingredients and their synonyms used in ingredients list, for ingredients and sub ingredients that are markers of transformed and ultra-transformed products. (edited) There are many other potential uses for the ingredients taxonomy, and some of them require more features like relations, properties etc.

For instance if we want to automatically translate ingredient lists, we need the entries in the taxonomy to have synonyms. That's something we can do incrementally, and we coud deploy partial translation.

If we want to automatically determine is a product is suitable for vegetarians or vegans, it's much more complex: we need to add properties to indicate if an ingredient can come from an animal source, and we need to do that for almost all ingredients: if there's 1 ingredient in the list of ingredients that we don't know about, then we can't determine anything. My preference is to try to take care of simple cases and impactful cases first, and as we do it, it will make the other uses easier to do later as well.

We can focus first on what has an immediate impact, then on what can have an impact soon. In the same line of thinking, I think we should try to find ways to do the most impactful things first.

The good thing is that it's easy to find those things: we just have to look at what we have in the ingredients lists of the products in the database. We sort them by frequency, and we work from top to down: we add them to the taxonomy if they are not there already, we had the parent ingredient if it has one, we try to identify synonyms and translations (using the ingredients lists from OFF) etc.

And as we do that, we can take note of potential issues, things that would be nice to support etc. But first let's focus on the general case. Then we can see how common the specific cases / issues are, what it would take to support them, what we could do if we supported them etc. A few specific points raised in the discussions above: Regarding parents: we should use them exclusively for the "is a" relationship. Not "contains", "derived from" etc. That means also that we don't add parents that are not actual ingredients themselves (e.g. no "milk derived ingredients" or "ingredients that contain milk"). Other relations can be added as properties, but before we do that and complexity the taxonomy, it would be good to think about the use cases, what they entail etc. Regarding synonyms: we should list only synonyms that we have found in ingredient lists, not synonyms from other sources / contexts. That's an issue we have in the additives taxonomy: early on, we added tons of synonyms that are just never used, or used only in other contexts (like the chemical formulas). --> those extra synonyms now hurt us when we do auto-correction of the spelling of additives The first synonym needs to be the most common one in ingredients lists. Regarding the language of the canonical entry: let's just stick with English, it will make everything much easier.