05-model.adoc

Model

The model that this Supermodel is based on is a small Semantic Web Ontology that defines the major model elements and other terminology used.

In this Specification, this implementation of the Supermodel model is presented via a series of "Levels" which are progressively more detailed, domain-specific, views of the model.

Level 0: Model Background

This view of the model is a backgrounding one which describes the underpinning model mecahnics that it uses. The object modelling used is based on the Web Ontology Language [OWL] and its own underlying use of RDF & RDFS ^[1]. The Provenance Ontology [PROV] is used to model real-world causal dependencies - provenance.

Diagram Key

The figure below is a key for the elements in all of the model diagrams in this document.

Figure 1. Diagram elements key

Object Modelling

The elements from the above subsection are shown in relation to one another in the figure below.

Figure 2. OWL objects and their relations

The elements shown above are identified with prefixed IRIs that correspond to entries in the Namespace Table. A short explanation of the diagram key elements is:

• owl:Class - represents any conceptual class of objects. Classes are expected to contain individuals - instances of the class - and the class, as a whole, may have realtions to other classes

• owl:NamedIndividual - an individual of an owl:class. For example, for the class ships, an individual might be Titanic

• rdf:property - a relationship between classes, individuals, or any objects and Literals

• rdfs:subClassOf - an rdf:property indicating that the domain (from object) is a subclass of the range (to objects). An example is the class student which is a subclass of person: all students are clearly persons but not vice versa

• rdf:type - the property that related an owl:NamedIndividual to the owl:Class that it’s a member of

• Literal - a simple literal data property, e.g. the string "Nicholas", or the number 42. Specific literal types are usually indicated when used

The remaining diagrams in this document use extensions to this basic model, for example Figure 3 uses colour-coded specialised forms of owl:Class (subclasses of it) and the relations in Figure 5 are specialised forms of rdf:property.

Provenance

General provenance/lineage information about anything - a rock sample, a dataset, a term in a vocabulary etc. - is described using the Provenance Ontology [PROV] which views everything in the world as being of one or more types in Figure 3.

Figure 3. PROV main classes and main relations

According to PROV, all things are either a:

• prov:Entity - a physical, digital, conceptual, or other kind of thing with some fixed aspects

• prov:Agent - something that bears some form of responsibility for an activity taking place, for the existence of an entity, or for another agent’s activity

• prov:Activity - something that occurs over a period of time and acts upon or with entities

While not often in front of mind for objects in any Data Domain, provenance relations always apply, for example: a sosa:Sample within the Sampling domain is a prov:Entity and will necissarily have been created via a sosa:Sampling which is a prov:Activity. Another example: an sdo:Person related to a dcat:Dataset via the property dcterms:creator in the DataCataloging domain is a specialised form of a prov:Agent related to a prov:Entity via prov:wasAttributedTo.

Level 1: Data Domains

The top-level view of the GA supermodel that assumes Level 0 background mechanics shows a set of 5 Data Domains which are:

1. Data Cataloguing

2. Organisations & People

3. Theming

4. Sampling & Observations

5. Spatial

These are shown in Figure 1 below.

Figure 4. Top-level view of the Supermodel showing Data Domains

These Data Domains are defined formally in a simple SKOS vocabulary within this model’s set of machine-readable resources. The vocabulary may be access ddirectly at https://linked.data.gov.au/def/supermodel/data-domains.

Elements at all other levels of detail in this model are classified according to these Data Domains by use of the dcat:theme property, for example, the class sosa:Sample is within the Sampling Data Domain, so it is defined as follows:

sosa:Sample
    a owl:Class ;
    dcat:theme super:sampling ;
    ...
.

Level 2: Central Classes

The next level of detail after the Data Domains introduces the Central Classes. Here the most significant, general, class per Data Domain is indicated, along with the main relationships between each of them. Figure 2 shows this.

Figure 5. Next level view of the Supermodel showing Central Classes

The Central Classes of each of the Data Domains are well-used classes from well-known models. For example, the Central Class of Organisation & People is [PROV]'s Agent class which is one of the three main classes of thing in PROV and used every time PROV is used to represent causal agents. PROV is used extensively to indicate how things - data, resources, systems - come to be.

A list of the Data Domains' Central Classes, their definitions, as given by their defining systems, and their defining system are given in Table 2 below.

Table 1. Data Domains their Central Classes and those Classes' definitions and origins

Data Domain	Central Class	Definition	Defined By
Data Cataloguing	dcat:Dataset	A collection of data that is listed in the catalog.	Data Catalog Vocabulary [DCAT]
Organisations & People	prov:Agent	An agent is something that bears some form of responsibility for an activity taking place, for the existence of an entity, or for another agent’s activity	PROV-O: The PROV Ontology [PROV]
Theming	skos:Concept	An idea or notion; a unit of thought	Simple Knowledge Organization System ontology [SKOS]
Observation & Sampling	sosa:Sample	A Sample is the result from an act of Sampling. Feature which is intended to be representative of a FeatureOfInterest on which Observations may be made. Physical samples are sometimes known as 'specimens'.	Sensor, Observation, Sample, and Actuator Ontology, within [SSN]
Spatial	geo:Feature	A discrete spatial phenomenon in a universe of discourse	GeoSPARQL Ontology [GEO]

The definitions of the main relations between Central Classes are given in

Table 2. Central Class main relations their definitions and origins

Central Class	Definition	Defined By
dcat:Dataset	A collection of data that is listed in the catalog.	Data Catalog Vocabulary [DCAT]
sosa:Sample	A Sample is the result from an act of Sampling. Feature which is intended to be representative of a FeatureOfInterest on which Observations may be made. Physical samples are sometimes known as 'specimens'.	Sensor, Observation, Sample, and Actuator Ontology, within [SSN]
geo:Feature	A discrete spatial phenomenon in a universe of discourse	GeoSPARQL Ontology [GEO]
skos:Concept	An idea or notion; a unit of thought	Simple Knowledge Organization System ontology [SKOS]
prov:Agent	An agent is something that bears some form of responsibility for an activity taking place, for the existence of an entity, or for another agent’s activity	PROV-O: The PROV Ontology [PROV]

Level 3: Domain Main Classes

At this level, the main classes within each Data Domain are identified and related to one another. In each Data Domain there is a well-known model used for the majority of the classes and relations. These well-known models are indicated to ensure that they can be followed if extensions to this level’s modelling need to be made.

Data Cataloguing

This subsection details the main elements of the Data Cataloguing Data Domain.

Figure 6. Domain Main Classes for Data Cataloguing

This Data Domain’s main classes are essentially the DCAT2 data model [DCAT] with a slight profiling: dcterms:hasPart should be used to indicate elements within catalogues (e.g. dcat:Dataset and other things within a dcat:Catalog) rather than the specialised properties of dcat:dataset because generic catalogue can be expected to catalogue many types of things and the type of the thing should be given by the thing, not the catalogue property used to indicate it.

Organisations & People

This subsection details the main elements of the Organisations & People Data Domain.

Figure 7. Domain Main Classes for Organisations & People

This Data Domain’s main classes are centered on [PROV]'s prof:Agent class but specific types of agent - person & organisation are defined using schema.org [SDO], the general-purpose ontology provisioned by Google, Microsoft & Yahoo for the description of web page data.

schema.org objects and properties are also used to define agents in the VocPub profile [VOCPUB] and are understood by ontology documentation tools such as pyLODE ^[2].

Theming

This subsection details the main elements of the Theming Data Domain.

Figure 8. Domain Main Classes for Theming

This Data Domain’s main classes are taken from [SKOS] and their expected/required properties and relations are formally defined in VocPub, a "vocabulary publication profile of SKOS" [VOCPUB]. VocPub just mandates certain vocabulary metadata and relations between elements in vocabularies. Conformance of vocabularies to VocPub is also easily testable using the profile’s validator and online tooling that support it ^[3].

Sampling & Observation

Spatial

This subsection details the main elements of the Spatial Data Domain.

Figure 9. Domain Main Classes for Spatial

This Data Domain’s main classes are taken directly from GeoSPARQL 1.1 [GEO] which is used extensively for Semantic Web spatial data already. GeoSPARQL’s main purposes are to relate things (geo:Feature) to their spatial projections - their geometries - and to relate things to one another - topological relations between features, such as within, touches, disjoint etc.

Particular datasets tend to implement specialised types of things (usually referred to as Feature Types) and sometimes specialised relations between things, e.g. special hydrological catchment feature type might relate to another by being upstream of it. This is as per modelling in the Geofabric ^[4].

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1. RDF: https://www.w3.org/RDF/, RDFS: https://www.w3.org/TR/rdf-schema/. These references generally need not be followed as descriptions of the use of OWL will cover their relevant concepts.

2. https://pypi.org/project/pyLODE/

3. The validator itself is online at https://w3id.org/profile/vocpub/validator and is pre-loaded into several online validation tools, for example Geoscience Australia’s vocabulary servers e.g. https://vocabs.ga.gov.au. It can also be selected for online validation use at https://rdftools.surroundaustralia.com

4. https://linked.data.gov.au/dataset/geofabric