Health Information modelling language - overview - Revision history

DavidStables: Replaced content with "Information on the health [https://wiki.endeavourhealth.org/index.php?title=Discovery_health_information_model#Information_model_language information language can be seen..."

2022-02-27T18:53:54Z

Replaced content with "Information on the health [https://wiki.endeavourhealth.org/index.php?title=Discovery_health_information_model#Information_model_language information language can be seen..."

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DavidStables: /* Identifiers, aliasing prefixes and context */

2021-09-26T14:26:44Z

Identifiers, aliasing prefixes and context

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 # VOCABULAR CONTEXT declaration for both IRIs and other tokens. This approach is used in JSON-LD which converts local JSON properties and objects into linked data identifiers via the @context keyword. This enables applications that know their context to use simple identifiers such as aliases.
 # MAPPING CONTEXT definitions for system level vocabularies. This provides sufficient context to uniquely identify a local code or term by including details such as the health care provider, the system and the table within a system. In essence a specialised class with the various property values making up the context.
 === Ontology - OWL2 DL ===

DavidStables: /* Identifiers, aliasing prefixes and context */

2021-09-26T14:24:10Z

Identifiers, aliasing prefixes and context

← Older revision		Revision as of 14:24, 26 September 2021
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	The following is an example of the use of the prefix directives for IRIs		The following is an example of the use of the prefix directives for IRIs
			<pre>
	@prefix : <http://www.DiscoveryDataService.org/InformationModel/Ontology#>.		@prefix : <http://www.DiscoveryDataService.org/InformationModel/Ontology#>.
	@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .		@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .

DavidStables: /* Sublanguages and syntaxes */

2021-09-26T14:18:32Z

Sublanguages and syntaxes

← Older revision		Revision as of 14:18, 26 September 2021
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	# MAPPING CONTEXT definitions for system level vocabularies. This provides sufficient context to uniquely identify a local code or term by including details such as the health care provider, the system and the table within a system. In essence a specialised class with the various property values making up the context.		# MAPPING CONTEXT definitions for system level vocabularies. This provides sufficient context to uniquely identify a local code or term by including details such as the health care provider, the system and the table within a system. In essence a specialised class with the various property values making up the context.

	The following is an example of the use of the prefix directives for IRIs ~~and to define aliases for some of the owl and rdfs tokens <pre style="background-color:#fcfaee">~~		The following is an example of the use of the prefix directives for IRIs
	@prefix : <http://www.DiscoveryDataService.org/InformationModel/Ontology#>.		@prefix : <http://www.DiscoveryDataService.org/InformationModel/Ontology#>.
	@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .		@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
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	@prefix sn: <ttp://snomed.info/sct#>.		@prefix sn: <ttp://snomed.info/sct#>.


	~~rdfs:subClassOf :alias ("subClassOf").~~

	~~owl:Class :alias ("Class").~~

	:alias		:alias

DavidStables: /* Foundation grammars and syntaxes - RDF, TURTLE and JSON-LD */

2021-09-26T14:16:43Z

Foundation grammars and syntaxes - RDF, TURTLE and JSON-LD

← Older revision		Revision as of 14:16, 26 September 2021
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	* JSON-LD representation, which can used by systems that prefer JSON, wish to use standard approaches, and are able to resolve identifiers via the JSON-LD context structure.		* JSON-LD representation, which can used by systems that prefer JSON, wish to use standard approaches, and are able to resolve identifiers via the JSON-LD context structure.
	*Proprietary JSON based object serializable grammar. This directly maps to the internal class structures used in Discovery and can be used by client applications that have a strong contract with a server.

	==== Identifiers, aliasing prefixes and context ====		==== Identifiers, aliasing prefixes and context ====

DavidStables: /* Requirements and Contributory languages */

2021-09-26T14:15:51Z

Requirements and Contributory languages

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	"owl:EquivalentClass" : [		"owl:EquivalentClass" : [
	{"owl:intersectionOf" :[		{"owl:intersectionOf" :[
	{ "@id": "Person"},		{ "@id": ":Person"},
	{ "owl:onProperty" : ":hasGender",		{ "owl:onProperty" : ":hasGender",
	"owl:somValuesFrom": {"@id":"Male"}},		"owl:somValuesFrom": {"@id":":Male"}},

	{ "owl:onProperty" : ":hasChild",		{ "owl:onProperty" : ":hasChild",
	"owl:~~somveValuesFrom~~" : {		"owl:someValuesFrom" : {
	"owl:intersectionOf": [		"owl:intersectionOf": [
	{ "@id":"Person"},		{ "@id":"Person"},
	{"owl:onProperty" : ":hasChild",		{"owl:onProperty" : ":hasChild",
	"owl:someValuesFrom" : {"@id":"Person"}}]]}}		"owl:someValuesFrom" : {"@id":":Person"}}]]}}
	</syntaxhighlight>		</syntaxhighlight>

DavidStables: /* Requirements and Contributory languages */

2021-09-26T14:14:17Z

Requirements and Contributory languages

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	JSON represents subjects , predicates and objects as object names and values with values being either literals or or objects.		JSON represents subjects , predicates and objects as object names and values with values being either literals or or objects.

	JSON itself has no inherent mechanism of differentiating between different types of entities and ~~therefor~~ JSON-LD is used. In JSON-LD identifiers resolve initially to @id and the use of @context enables prefixed IRIs and aliases.		JSON itself has no inherent mechanism of differentiating between different types of entities and therefore JSON-LD is used. In JSON-LD identifiers resolve initially to @id and the use of @context enables prefixed IRIs and aliases.

	The above Grandfather can be represented in JSON-LD (context not shown) as follows:<syntaxhighlight lang="json-ld" style="border:3px solid grey">		The above Grandfather can be represented in JSON-LD (context not shown) as follows:<syntaxhighlight lang="json-ld" style="border:3px solid grey">

DavidStables: /* Requirements and Contributory languages */

2021-09-26T14:13:05Z

Requirements and Contributory languages

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	A model presented in the human legible grammar must be translatable directly to the machine representation without loss of semantics. ~~In the ensuing paragraphs the human optimised grammar is illustrated but in the final language specification both are presented side by side to illustrate semantic translatability.~~		A model presented in the human legible grammar must be translatable directly to the machine representation without loss of semantics.

	'''Example (OWL2)'''		'''Example (OWL2)'''

DavidStables: /* Requirements and Contributory languages */

2021-09-26T14:12:00Z

Requirements and Contributory languages

← Older revision		Revision as of 14:12, 26 September 2021
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	* [https://www.w3.org/TR/shacl/ SHACL]. Used for everything that defines the shape of data where a closed world assumption is required. Although SHACL is designed for validation of RDF, as SHACL describes what things 'should be' it can be used as a data modelling language		* [https://www.w3.org/TR/shacl/ SHACL]. Used for everything that defines the shape of data where a closed world assumption is required. Although SHACL is designed for validation of RDF, as SHACL describes what things 'should be' it can be used as a data modelling language
	* [https://www.w3.org/TR/sparql11-query/ SPARQL] Used as the logical means of querying model conformant data (not to be confused with the actual query language used which may be SQL)		* [https://www.w3.org/TR/sparql11-query/ SPARQL] Used as the logical means of querying model conformant data (not to be confused with the actual query language used which may be SQL)
			* RML, an extension of R2RML, used to map between RDF and other formats such as RDBMS, JSON or CSV.


	A model presented in the human legible grammar must be translatable directly to the machine representation without loss of semantics. In the ensuing paragraphs the human optimised grammar is illustrated but in the final language specification both are presented side by side to illustrate semantic translatability.		A model presented in the human legible grammar must be translatable directly to the machine representation without loss of semantics. In the ensuing paragraphs the human optimised grammar is illustrated but in the final language specification both are presented side by side to illustrate semantic translatability.

DavidStables: /* Purpose background and rationale */

2021-09-26T13:43:09Z

Purpose background and rationale

← Older revision		Revision as of 13:43, 26 September 2021
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	# A modelling language using the World wide web semantic languages that can be used to exchange all elements of the model.		# A modelling language using the World wide web semantic languages that can be used to exchange all elements of the model.

	The main purpose of a modelling language is to exchange data and information about [[Discovery health information model\|information models]] in a way that both machines and humans can understand. A language must be able to describe the types of structures that make up an information model ~~as can be seen on the right~~. Diagrams and pictures are all very well, but they cannot be used by machines.		The main purpose of a modelling language is to exchange data and information about [[Discovery health information model\|information models]] in a way that both machines and humans can understand. A language must be able to describe the types of structures that make up an information model. Diagrams and pictures are all very well, but they cannot be used by machines.

	It is necessary to support both human and machine readability so that a model can be validated both by humans and computers. Humans can read diagrams or text. Machines can read data bits. The two forms can be brought together as a stream of characters forming a language.		It is necessary to support both human and machine readability so that a model can be validated both by humans and computers. Humans can read diagrams or text. Machines can read data bits. The two forms can be brought together as a stream of characters forming a language.