separate doc; orig loci diag in intro

02-preamble.adoc

@@ -0,0 +1,98 @@
+== Preamble
+
+=== Abstract
+
+This Model - the Loc-I Supermodel - is the link:http://www.ga.gov.au/locationindex[Location Index (Loc-I) Projects]'s overarching data model that provides integration logic for all Loc-I elements.
+
+=== Namespaces
+
+This model is built on a "baseline" of Semantic Web models which use a variatey of namespaces. Prefixes for thess namespaces, used througout this document, are listed below.
+
+[id=tbl-prefixes, width=75%, frame=none, grid=none]
+.Namespaces
+|===
+|Prefix | Namespace | Description
+
+| **`super`** | **`https://linked.data.gov.au/def/loci-supermodel/`** | **this model**
+|`dcterms:` | `http://purl.org/dc/terms/` | Dublin Core Terms vocabulary namespace
+|`ex:` | `http://example.com/` | Generic examples namespace
+|`owl:` | `http://www.w3.org/2002/07/owl#` | Web Ontology Language ontology namespace
+|`rdfs:` | `http://www.w3.org/2000/01/rdf-schema#` | RDF Schema ontology namespace
+|`sosa:` | `http://www.w3.org/ns/sosa/` | Sensor, Observation, Sample, and Actuator ontology namespace
+|`skos:` | `http://www.w3.org/2004/02/skos/core#` | Simple Knowledge Organization System (SKOS) ontology namespace
+|`time:` | `http://www.w3.org/2006/time#` | Time Ontology in OWL namespace
+|`void:` | `http://rdfs.org/ns/void#` | Vocabulary of Interlinked Data (VoID) ontology namespace
+|`xsd:` | `http://www.w3.org/2001/XMLSchema#` | XML Schema Definitions ontology namespace
+|===
+
+=== Terms & Definitions
+
+The following terms appear in this document and, when they do, the definitions in this section apply to them.
+
+[id=central-class]
+Central Class::
+
+`http://pid.geoscience.gov.au/def/sm/terms-definitions/central-class`
++
+Central Classes are the generic data classes at the centre of Data Domains with high-level relationships between them defined in this supermodel. 
++
+These classes are taken from general standards - usually well-known international stadnards - and specialised and extended within implementation scenarios to cater for specific needs.
+
+[id=data-domain]
+Data Domain::
+
+`http://pid.geoscience.gov.au/def/sm/terms-definitions/data-domain`
++
+High-level conceptual areas within which Geosicence Australia has data. 
++
+These Data Domains are not themed scientificly - 'geology', 'hydrogeology', etc. - but instead based on parts of the _Observations & Measurement_ <<ISO19156>> standard, realised in Semantic Web form in the SOSA Ontology, part of the _Semantic Sensor Network Ontology_ <<SSN>>.
++
+Current Data Domain are shown in <<fig-top-level, Figure 1>>.
+
+[id=knowledge-graph]
+Knowledge Graph::
+
+`http://pid.geoscience.gov.au/def/sm/terms-definitions/knowledge-graph`
++
+A Knowledge Graph is a dataset that uses a graph data tructure - nodes and edges - with strongly-defined elements.
+
+[id=linked-data]
+Linked Data::
+
+`http://pid.geoscience.gov.au/def/sm/terms-definitions/linked-data`
++
+A set of technologies and conventions defined by the https://www.w3.org[World Wide Web Consortium] that aim to present data in both human- and machine-readable form over the Internet. 
++
+Linked Data is strongly-defined with each element having either a local definition or a link to an available definition on the Internet.
++
+Linked Data is graph-based in nature, that is it consistes of nodes and edges that can forever be linked to further conceps with defined relationships.
++
+-- https://www.w3.org/standards/semanticweb/data
+
+[id=location-index]
+Location Index::
+
+`http://pid.geoscience.gov.au/def/sm/terms-definitions/location-index`
++
+A project aiming to provide a consistent way to seamlessly integrate spatial data from distributed sources.
++
+-- http://www.ga.gov.au/locationindex[Location Index Project Website]
+
+[id=semantic-web]
+Semantic Web::
+
+`http://pid.geoscience.gov.au/def/sm/terms-definitions/semantic-web`
++
+The https://www.w3.org[World Wide Web Consortium]'s vision of an Internet-based web of Linked Data. 
++
+Semantic Web is used to refer to something more than just the technologies and conventions of Linked Data; the term also encompases a specific set of interoperable data models - often called ontologies - published by the W3C, other standards bodies and some well-known companies.
++
+The 'semantic' refers to the strongly-defined nature of the elements in the Semantic Web: the meaning of Semantic Web data is as precicely defined as any data can be.
++
+-- https://www.w3.org/standards/semanticweb/
+
+=== Conventions
+
+All model diagrams use elements introduced in <<#fig-level0-key, 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>>.

03-introduction.adoc

@@ -0,0 +1,23 @@
+== Introduction
+
+=== Loc-I Project
+
+The Location Index (LOC-I) project, established in 2018, created a framework to provide 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) may be included as well as non-government data.
+
+See the project website, http://www.ga.gov.au/locationindex, for more project information.
+
+=== Loc-I Technical Implementation 
+
+The technical implementation of Loc-I was based on Semantic Web <<semantic-web>> principles allowing for datasets to be published as Linked Data <<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.
+
+<<#orig-arch, 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. 
+
+[[orig-arch]]
+.Original Loc-I Detailed Architecture (from https://www.csiro.au[CSIRO])
+image::/img/original-loci-detailed-architecture.png[Original Loc-I Detailed Architecture]
+
+=== Supermodel Specification role
+
+This Loc-I Supermodel Specification 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 to allow Loc-I information to be exported in forms expected by industry.

04-model.adoc

@@ -0,0 +1,169 @@
+== 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 footnote:[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.]. 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.
+
+[id=fig-level0-key]
+.Diagram elements key
+image::img/key.png[]
+
+==== Object Modelling
+
+The elements from the above subsection are shown in relation to one another in the figure below.
+
+[id=fig-level0-owl]
+.OWL objects and their relations
+image::img/level0-owl.png[]
+
+The elements shown above are identified with prefixed IRIs that correspond to entries in the <<#tbl-prefixes, 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 <<#fig-level0-prov, Figure 3>> uses colour-coded specialised forms of `owl:Class` (subclasses of it) and the relations in <<#fig-central-classes, 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 <<#fig-level0-prov, Figure 3>>.
+
+[id=fig-level0-prov]
+.PROV main classes and main relations
+image::img/level0-prov.png[]
+
+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 supermodel that assumes Level 0 background mechanics shows a set of 5 <<#data-domain, Data Domains>> which are:
+
+1. http://pid.geoscience.gov.au/def/sm/data-domains/data-cataloguing:[Data Cataloguing]
+2. http://pid.geoscience.gov.au/def/sm/data-domains/organisations-people:[Organisations & People]
+3. http://pid.geoscience.gov.au/def/sm/data-domains/spatiality:[Spatiality]
+4. http://pid.geoscience.gov.au/def/sm/data-domains/theming:[Theming]
+
+These are shown in <<fig-top-level, Figure 1>> below.
+
+[id=fig-top-level]
+.Top-level view of the Supermodel showing Data Domains
+image::img/data-domains.png[]
+
+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 http://pid.geoscience.gov.au/def/sm/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:
+
+```turtle
+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. <<fig-central-classes, Figure 2>> shows this.
+
+[id=fig-central-classes]
+.Next level view of the Supermodel showing Central Classes
+image::img/central-classes.png[]
+
+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 <<tbl-central-classes, Table 2>> below.
+
+[#tbl-central-classes, width=75%, frame=none, grid=none]
+.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>>
+| 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>>
+| Spatiality | `geo:Feature` | A discrete spatial phenomenon in a universe of discourse | GeoSPARQL Ontology <<GEO>>
+| Theming | `skos:Concept` | An idea or notion; a unit of thought | Simple Knowledge Organization System ontology <<SKOS>>
+| 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>>
+|===
+
+The definitions of the main relations between Central Classes are given in 
+
+[#tbl-cc-relations, width=75%, frame=none, grid=none]
+.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>>
+| 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>>
+| `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.
+
+[id=fig-domain-classes-data-cataloguing]
+.Domain Main Classes for Data Cataloguing
+image::img/domain-classes-data-cataloguing.png[]
+
+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.
+
+[id=fig-domain-classes-organisations-people]
+.Domain Main Classes for Organisations & People
+image::img/domain-classes-organisations-people.png[]
+
+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 footnote:[https://pypi.org/project/pyLODE/].
+
+==== Spatiality
+
+This subsection details the main elements of the Spatiality Data Domain.
+
+[id=fig-domain-classes-spatiality]
+.Domain Main Classes for Spatiality
+image::img/domain-classes-spatiality.png[]
+
+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 footnot:[https://linked.data.gov.au/dataset/geofabric].
+
+==== Theming
+
+This subsection details the main elements of the Theming Data Domain.
+
+[id=fig-domain-classes-theming]
+.Domain Main Classes for Theming
+image::img/domain-classes-theming.png[]
+
+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 footnote:[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].

05-domain-details.adoc

@@ -0,0 +1,13 @@
+== Data Domain Details
+
+The Data Domains described above are implemented using a number of resources. The following subsections link to these resources.
+
+=== Data Cataloguing Details
+
+=== Organisations & People Details
+
+=== Sampling Details
+
+=== Spatiality Details
+
+=== Theming Details