# FAQ: Modeling

## What is the difference between is_a and mixins?

LinkML allows any class to have:

• zero or one is_a parents, declared using the is_a slot

• any number of mixin parents, declared using the mixins slot

Semantically these are the same - all inheritable slots are inherited through is_a and mixins.

Classes should have a single inheritance backbone, with is_a representing the “main” parent. Generally the mixin and is_a hierarchies should be stratified.

## Didn’t you know composition is favored over inheritance these days?

Wikipedia composition over inheritance

We have certainly seen cases where inheritance is abused in programming languages, especially when it comes to behavioral classes.

However, in our experience inheritance is still very useful when used for data classes. We trust the users of LinkML to create schemas to design schemas carefully.

## When should I use attributes vs slots?

The attributes metamodel slot is really just a convenient shorthand for being able to declare slots “inline”.

LinkML treats slots as first class entities. They are defined in their own section of a schema, and can be reused by any number of classes (and refined, using slot_usage). This can be very powerful for reuse.

However, this can also be slightly inconvenient for simple schemas, especially those where we have classes with slots that are completely “owned” by that class. The attribute slot can be used to avoid having to specify the slot separately

## What are induced slots?

Because the same slot can be reused in different classes (with each class potentially refining semantics using slot_usage), it can be useful to give a new “name” for the implicit class-specific version of that slot.

For example, if you have a slot name, and this is used in classes Person and Organization, and these are refined for each class (for example, “Organization” may refine the name slot to follow a particular regular expression). In some generators such as the markdown generator, you will see “induced” slots such as Organization_name.

The extent to which these are made visible is currently the subject of some discussion, see GitHub for details.

## Why would I need to define my own types?

Types are scalar values such as integers or strings. They are also known as “literals” in RDF.

Strictly speaking it is not necessary to define your own types, you can just use the builtin types.

However, defining your own types can be good practice, as it can make your intent clearer. For example, if you have a slot description you may want to specify the range as your own type NarrativeText that maps to string behind the scenes. But this provides additional cues, e.g. that the value of this field is intended to be human-readable text.

Some applications may choose to interpret this in particular ways. E.g. you may want to define all narrative text fields as being amenable to spellchecking, or machine learning natual language processing, or special kinds of indexing in ElasticSearch/Solr

## Why would I want to use enums over strings?

Enums provide a more controlled vocabulary than strings. You can validate categorical fields using enums, whereas with basic strings you don’t have a built in way of validating if a string is valid.

Enums also give you hooks into ontologies and vocabulaies.

More on enums:

## How do I do the equivalent of JSON-Schema composition?

See: Schema Composition in JSON-Schema docs

Currently there is no direct analog of JSON-Schema anyOf/allOf/oneOf schema composition structures.

In some cases, the equivalent of this can be achieved through inheritance in LinkML.

LinkML also has the union_of slot to allow an exhaustive set of subclasses to be specified. This acts in a similar way to oneOf and future versions of JSON-Schema translation may compile down to oneOf

## Why are my class names translated to CamelCase?

LinkML allows you to use any convention you like when authoring schemas. However, when translating to other formalisms such as JSON-Schema, RDF, Python then those naming conventions are applied.

For example, if you define a class:

default_prefix: my_schema

classes:
my class:
attributes:
my slot:


Then this will translate as follows:

• Python, JSON-Schema

• MyClass

• my_slot

• RDF/OWL URIs

• my_schema:MyClass

• my_schema:my_slot

Note in the RDF/OWL representation, seperate rdfs:label triples will be generated retaining the original human-friendly name.

This has the advantage of keeping human-friendly nomenclature in the appropriate places without specifying redundant computer names and human names

However, the autotmatic translation can be confusing, so some schemas opt to follow standard naming conventions in the schema:

default_prefix: my_schema

classes:
MyClass:
attributes:
my_slot:


you have the option of specifying human-friendly titles for each element:

default_prefix: my_schema

classes:
MyClass:
title: my class
attributes:
my_slot:
title: my slot


Note that one current limitation of the LinkML generator framework is that it does not protect you from using keywords that are reserved in certain formalisms.

For example, if you define a slot in, then this conflicts with the Python keyword, and the generated python code will raise errors. For now the recommendation is to avoid these as they arise.

In future, the LinkML framework will

• warn if a reserved term is used

• provide a mechanism for transparent mapping between a schema element and a “safe” version of the element