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For this reason, it is important that the vocabularies and profiles you create are semantically equivalent to what you intend them to be. It is not enough to describe with human-readable annotations what your classes, attributes, associations and constraints mean, but necessary to specify this human-readable intent with logic. This document helps you to achieve this goal.

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X.1. The Linked Data Modeling paradigm

First you need to consider the type of model you are creating. When modeling on the FI-Platform, you should not think in the traditional terms of conceptual, logical and physical models, nor in the terms of typical UML profiles. Models created on the FI-Platform can be used for these purposes, but the RDF-based knowledge representation language used here has more expressivity than typical data description languages.

When modeling on the FI-Platform, you should keep in mind these essential principles:

You're working with a graph

The RDF data model is a very generalized graph which is able to describe many kinds of data structures. Both data models and instance data are described with the same structure: triples of two nodes and an edge connecting them. RDF graphs can be represented in a very simple three column tabular form: <subject, predicate, object>. Each subject and object are some entities, or resources in the linked data jargon (for example classes or instances of classes, literal values etc.) and predicates are entities that link them together. For example a subclass association between classes A and B would be represented as <B, subclass, A>, or visually as two nodes in a graph linked by a subclass edge. Attribute values are represented with the same structure, with the attribute entity acting as the edge and the literal attribute value acting as the object node: <A, someAttribute, "foobar">.

Polyhierarchies are supported

In traditional data modeling multiple inheritance (typically the only way to represent hierarchical structures) is typically not allowed or at least severely limited. Instead, building hierarchies with multiple superclasses is allowed and in some cases even necessary.

All entities have identities

Usually some entities in data modeling languages don't have an identity as they are inherently part of their defining entities. As an example, UML attributes are not entities that can be individually referenced, they exist only as part of the class that defined them. This means that a model might have multiple attributes with the same identifying name and meaning but there is no technical way to straightforwardly identify these attributes as being "the same".

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Resource identifiers can generally be minted (declared) as anything that adheres to the URI RFC 3986, but on the FI-Platform minting is controlled by enforcing a namespace which is under https://iri.suomi.fi/. This ensures that model resources will not accidentally collide with resources elsewhere on the web.

No strict separation between data and metadata

Due to the abovementioned identifiers, it is possible to add descriptive metadata to any entity either by stating the metadata by the entity itself, or externally by referring to the entity by its identifier.

No strict separation between classes and instances

So-called punning means that classes can act also as instances, there is no hard line separating them (though this doesn't mean the situation would be ambiguous, there are clear logical rules for deducing the state of affairs).

No strict separation between conceptual and logical model

When properly annotated, the actual model itself acts as a machine-readable conceptual model with a rich layer of logical model features on top of it. This also applies to schemas (application profiles), where the schema itself can be directly annotated. The conceptual link can be achieved for example by describing the entities conceptually as a SKOS vocabulary (also RDF-based) and referring to the SKOS concepts from the data model (thus creating a machine-readable link between the terminological and logical models).

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X.2. Which one to create: a Core Vocabulary or Application profile?

Which model type should you start with? This naturally depends on your use-case. You might be defining a database schema, building a service that distributes information products adhering to a specific schema, trying to integrate two datasets... In general, all these and other use-cases start with the following workflow:

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• If you want to annotate data, check its logical soundness or infer new facts from it, you need a core vocabulary. With a core vocabulary you are essentially making a specification stating "individuals that fit these criteria belong to these classes".
• If you want to validate the data structure or do anything you'd traditionally do with a schema, you need an application profile. With an application profile you are essentially making a specification stating "graph structures matching these patterns are valid".

Core Vocabularies in a Nutshell

As mentioned, the idea of a Core Vocabulary is to describe semantically the resources (entities) you will be using to describe your data with. In other words, what typically ends up as a conceptual model documentation or diagram, is now described by a formal model.

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The OWL language has multiple profiles for different kind of inferencing. The one currently selected for the FI-Platform (OWL 2 EL) is computationally simple, but still logically expressive enough to fulfill most modeling needs. An important reminder when doing core vocabulary modeling is to constantly ask: is the feature I am after part of a specific use case (and thus application profile) or is it essential to the definition of these concepts?

Application Profiles in a Nutshell

Application profiles fill the need to not only validate the meaning and semantic consistency of data and specifications, but to enforce a specific syntactic structure and contents for data.

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Following the key Semantic Web principles, SHACL validation is not based on whitelisting (deny all, permit some) like traditional closed schema definitions. Instead, SHACL works by validating the patterns we are interested in and ignoring everything else. Due to the nature of RDF data, this doesn't cause problems, as we can simply dump all triples from the dataset that are not part of the validated patterns. Also, it is possible to extend SHACL validation by SHACL-SPARQL or SHACL Javascript extensions to perform a vast amount of pre/postprocessing and validation of the data, though this is not currently supported by the FI-Platform nor within the scope of this document.

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X.3 Core Vocabulary modeling

When modeling a core vocabulary, you are essentially creating three types of resources:

Attributes

Attributes are in principle very similar to attribute declarations in other data modeling languages. There are some differences nevertheless that you need to take into account:

1. Attributes can be used without classes. For an attribute definition, one can specify rdfs:domain and/or rdfs:range. The domain refers to the subject in the <subject, attribute, literal value> triple, and range refers to the literal value. Basically what this means is that when such a triple is found in the data, its subject is assumed to be of the type specified by rdfs:domain, and the datatype is assumed to be of the type specified by rdfs:range.
2. The attribute can be declared as functional, meaning that when used it will only have at most one value. As an example, one could define a functioanl attribute called age with a domain of Person. This would then indicate that each instance of Person can have at most one literal value for their age attribute. On the other hand, if the functional declaration is not used, the same attribute (e.g. nickname) can be used to point to multiple literal values.
3. Attribute datatypes are by default XSD datatypes, which come with their own datatype hierarchy (see here).
4. In core vocabularies it is sometimes preferable to define attribute datatype on a very general level, for example as rdfs:Literal. This allows using the same attribute in a multitude of application profiles with the same intended semantic meaning but enforcing a context-specific precise datatype in each application profile.
5. Attributes can have hierarchies. This is an often overlooked but useful feature for inferencing. As an example, you could create a generic attribute called Identifier that represents the group of all attributes that act as identifiers. You could then create sub-attributes, for example TIN (Tax Identification Number), HeTu (the Finnish personal identity code) and so on.
6. Attributes can have explicit equivalence declarations (i.e. an attribute in this model is declared to be equivalent to some other attribute).

Associations

Associations are similarly not drastically different compared to other languages. There are some noteworthy things to consider nevertheless:

1. Associations can be used without classes as well. The rdfs:domain and rdfs:range options can here be used to define the source and target classes for the uses of a specific association. As an example, the association hasParent might have Person as both its domain and range, meaning that all triples using this association are assumed to describe connections between instances of Person.
2. Associations in RDF are binary, meaning that the triple <..., association, ...> will always connect two resources with the association acting as the predicate.
3. Associations can have hierarchies similarly to attributes.
4. Associations have flags for determining whether they are reflexive meaning that both the subject and object of the association are assumed to be the same resource, whether they are transitive (meaning that if classes A and B as well as B and C are connected by association X, then this is equivalent to declaring that class A is connected to C by association X).
5. Associations can have explicit equivalence declarations (i.e. an association in this model is declared to be equivalent to some other association).

Classes

Classes form the most expressive backbone of OWL. Classes can simply utilize the rdfs:subClassOf association to create hierarchies, but typically classes contain property restrictions - in the current FI-Platform case really simple ones. A class can simply state existential restrictions requiring that the members of a class must contain specific attributes and/or associations. Further cardinality restrictions are not declared here, as the chosen OWL profile does not support them, and cardinality can be explicitly defined in an application profile. In order to require specific associations or attributes to be present in an instance of a class, they must exist, as associations and attributes are never owned by a class, unlike in e.g. UML. They are individual definitions that are simply referred to by the class definition. This allows for situations where an extremely common definition (for example a date of birth or surname) can be defined only once in one model and then reused endlessly in all other models without having to be ever redefined.

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Similarly to associations and attributes, classes have equivalence declarations. Additionally, classes can be declared as non-intersecting. It is important to understand that classes being sets doesn't by default in any way force them to be strictly separated. From the perspective of the inference reasoner, classes for inanimate objects and people could well be overlapping, unless it is explicitly declared logically inconsistent. With a well laid out class hierarchy, simply declaring a couple of superclasses as non-intersecting will automatically make all their subclasses non-intersecting as well.

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X.4. Application profile modeling 

With application profiles we use strictly separate set of terms to avoid mixing up the core vocabulary structures we are validating and the validating structures themselves. The application profile entities are called restrictions:

Attribute and association restrictions

These restrictions are tied to specific attribute and association types that are used in the data being validated. Creating a restriction for a specific core vocabulary association allows it to be reused in one or more class restrictions. In the future the functionality of the FI-Platform might be extended to cover using attribute and association restrictions individually without class restrictions, but currently this is not possible.

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For association restrictions, the currently supported extra restriction is the class type requirement for the association restriction target (i.e. what type of an instance must be at the object end of the association).

Class restrictions

Similarly to core vocabulary classes, also class restrictions utilize a group of predefined attribute and association definitions. Again, this allows for example the specification of some extremely reusable association and attribute restrictions which can then be reused a multitude of times in various application profiles.

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Class restrictions don't operate in a set-theoretical manner like core vocabulary definitions, but there is a way to implement "inheritance" in validated classes. If a class restriction utilizes another class restriction, its target classes contents are checked against both of these class restrictions.

General word of caution on modeling

SHACL is a very flexible language and due to this nature it allows the creation of validation patterns that might seem legit but are actually unsatisfiable by any instance data. As an example, the utilization of other class restrictions might lead to a situation where an attribute can never be validated as it is required to conform to two conflicting datatypes at the same time.

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