Loc-I Supermodel Specification

This Model - the Loc-I Supermodel - is the Location Index (Loc-I) Project's overarching data model that provides integration logic for all Loc-I elements. It is based on the general-purpose Supermodel Model.

This model is built on a "baseline" of Semantic Web models which use a variatey of namespaces. Prefixes for these namespaces, used througout this document, are listed below.

The following terms appear in this document and, when they do, the definitions in this section apply to them.

These terms are presented as a formal Semantic Web vocabulary at

All model diagrams use elements introduced in Figure 1. These elements are defined in the RDF, RDFS and OWL ontologies, see [OWL] for mode details.

All code snippets in this document, used to show formal and machine-readable versions of concepts, are expressed using the Turtle RDF syntax [TTL].

The Location Index (LOC-I) project, established in 2018, created a methodology and data models framework providing for a consistent way to seamlessly integrate spatial data from distributed sources. The target was Australian spatial data "of national significance", meaning most - initially all - of the data considered was Australian Federal government data. Going forward, Loc-I is not limited to this sort of data, so other government data (States, Local) as well as non-government data may be included.

See the project website, http://www.ga.gov.au/locationindex, for more project information.

The technical implementation of Loc-I was based on Semantic Web principles allowing for datasets to be published as Linked Data independently by data holders - different government departments, companies etc. - and consumed with minimal effort required for integration.

The technical implementation relied on a Loc-I Ontology, the main Loc-I model, multiple Background Models, fundamental, standards, data models that the Loc-I Ontology depended on, and, for some datasets, Dataset Models of their content that spefialise the Loc-I Ontology.

Figure 1 below shows the original detailed architecture diagram used to explain Loc-I’s parts from 2018 - 2021. This supermodel document does not detail the technical implementation of Loc-I elements but does provide a formal, integrated, model for all the elements in that figure. The OWL ontology elements represented on the left are all included in this supermodel unchanged, however several additional background ontologies and profiles have been added to better integrate Loc-I datasets' models. These include the OGC LD API profile [OGCLDAPI] wich is used to ensure data meets, or to build out data to meet, requirements of the Open Geospatail Consortium’s OGC API - Features [OGCAPI] which is now used as the standard API for Loc-I datasets. That standard was not available when the original Loc-I project was started in 2018.

This Loc-I Supermodel Specification document formally defines the technical model implementation of Loc-I including all previously created models and relations as well as new models that have been added since the original Loc-I project, for example data models for new Loc-I datasets.

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.

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

These are shown in Figure 1 below.

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:

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.

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.

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

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.

This model is based on the Data Catalog Vocabulary [DCAT] and The Provenance Ontology [PROV] with extensions to cater for mappings to FAIR [FAIR] and CARE [CARE] models. The essential model is shown in Figure 8.

This domain is essentially the organization modelling element of schema.org with a few additional properties to track some aspects of organisations relevant to data governance. The essential model is shown in Figure 10.

still to come

This domain is essentially the Data Cube Vocabulary [DQ]'s core elements with dataset metadata replaced with elements from DCAT [DCAT]. The essential model is shown in Figure 9.

The Spatiality domain is the promary concern of the Loc-I Project since the project aims to provide an integration mthdology, models and an integrated and estensible "spine" of major national spatial datasets.