telicent-ies-tool

telicent-ies-tool

A library for working with the IES data standard.

IES is a UK Government data exchange standard. It is a 4D ontology specified as an RDF Schema. The purpose of telicent-ies-tool is to make it easier for users to create compliant IES data.

Formal ontologies can be hard for beginners to grasp. Unlike traditional data models, they are constructional in nature. Instead of defining a set of entities and fields to populate, ontologies like IES define a set of common objects that can be assembled following well-defined patterns. This introduces a degree of flexibility not found in traditional data models, which is great for real-world situations where information requirements change faster than the data models can be re-engineered. The disadvantage to this approach is that if users and developers are not steeped in the principles that underpin the ontology (4D, extensional, constructional) then it is possible to generate structures that do not follow the patterns. We can mitigate this somewhat with the use of SHACL, but overuse of SHACL would result in the loss of desired flexibility.

To counter some of these issues, Telicent have collected together a number of convenience functions that we have used on projects into one Python library to help data engineers get started and hopefully ensure appropriate use of the ontology. This is work in progress, and we want to hear from users about what is missing / not working, and of course requests for new features. We will continue to add functionality as we identify the requirement in customer projects.

Dependencies

Python >= 3.8

Install

pip install telicent-ies-tool

Overview & Approach

The IES Tool has a main factory class - IESTool - that takes care of storage, caching, and Python object instantiation. This will be automatically initiated as the default IES_TOOL constant which can be imported It is necessary to initiate a tool object for every dataset you wish to work with.

from ies_tool.ies_tool import IESTool, IES_TOOL

As well as the main factory class, and the default instance of the tool (IES_TOOL), there are base Python classes for all the significant IES classes:

• RdfsResource
  • RdfsClass
    • ClassOfElement
      • Representation
        • Identifier
        • Name
        • MeasureValue
      • Measure
    • ClassOfClassOfElement
      • UnitOfMeasure
      • NamingScheme
  • ExchangedItem
    • ClassOfElement
      • Representation
        • Identifier
        • Name
        • MeasureValue
      • Measure
    • ClassOfClassOfElement
      • UnitOfMeasure
      • NamingScheme
  • Element
    • Entity
      • Device
      • Location
        • GeoPoint
      • ResponsibleActor
        • Post
        • Person
        • Organisation
    • State
      • DeviceState
      • BoundingState
      • BirthState
      • DeathState
      • EventParticipant
    • Event
      • Communication
      • PartyInCommunication
    • ParticularPeriod

Each of these classes can be instantiated using typical Pythonic approach - e.g.

anne = Person(given_name="Anne", surname="Smith")

Note that the default IES_TOOL instance will be used unless you specify the tool= parameter in the class initiation you're using so it knows where to create the RDF data. It is recommended that this approach is used in most cases.

However, data can also be created using the instantiate() method on IESTool, the tool will attempt to determine the most appropriate Python base class to initiate - e.g.

fred = tool.instantiate(classes=['http://ies.data.gov.uk/ontology/ies4#Person'])

The 'fred' object returned will be a Python Person object. It's generally better to just initiate the classes directly, as it is not always possible to deterministically infer the Python class from the instantiate() call. Developers can override the inference by setting the base_class parameter - e.g.

Usage

To import, use:

from ies_tool.ies_tool import IESTool, IES_TOOL

This will import a pre-initiated IESTool object that is ready to use, and will be the default factory object for all other classes

To manually instantiate another tool (factory) object:

tool = IESTool()

Creating Data

Person

To create a person we need to instantiate a Person object first. We can pass in several parameters, but the only mandatory one is the tool parameter which refers to the IESTool factory object being used. The instantiated person graph will be stored in tool dataset.

my_person = Person(
    given_name='Fred',
    surname='Smith',
    date_of_birth="1985-08-21"
)

Note that start is used for the date of birth as this is inherited from Element. This person instance will be created in the default IES_TOOL.

We can then add additional information associated with the person using one of the available methods such as add_identifier(), add_state(), in_location(), works_for() etc.

The data produced from the example above will be:

@prefix : <http://example.com/rdf/testdata#> .
@prefix ies: <http://ies.data.gov.uk/ontology/ies4#> .
@prefix iso8601: <http://iso.org/iso8601#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

:d8751056-e1c2-40b7-86f6-670e50e450f1-1_BIRTH a ies:BirthState ;
    ies:inPeriod iso8601:1985-08-21 ;
    ies:isStartOf :d8751056-e1c2-40b7-86f6-670e50e450f1-1 .

:d8751056-e1c2-40b7-86f6-670e50e450f1-1 a ies:Person ;
    ies:hasName :d8751056-e1c2-40b7-86f6-670e50e450f1-1_FIRSTNAME,
        :d8751056-e1c2-40b7-86f6-670e50e450f1-1_SURNAME .

:d8751056-e1c2-40b7-86f6-670e50e450f1-1_FIRSTNAME a ies:GivenName ;
    ies:representationValue "Fred"^^xsd:string .

:d8751056-e1c2-40b7-86f6-670e50e450f1-1_SURNAME a ies:Surname ;
    ies:representationValue "Smith"^^xsd:string .

iso8601:1985-08-21 a ies:ParticularPeriod ;
    ies:iso8601PeriodRepresentation "1985-08-21"^^xsd:string .

Note that the URIs are generated automatically (UUIDs appended to the default data namespace) unless the uri parameter is used to manually set them.

Low-level operations (RDF / RDFS)

The base classes provide some simple methods for creating predicates:

my_person.add_literal(predicate="http://xmlns.com/foaf/0.1/name",literal="Fred Smith")
my_person.add_label("Freddy")
my_person.add_comment("The one and only Fred Smith")
my_person.add_telicent_primary_name("SMITH, Fred")
my_person.add_related_object(predicate="http://ies.data.gov.uk/ontology/ies4#ancestorOf",related_object=my_other_person)

The IES tool itself also provides a set of low-level methods for working with the graph, such as add_triple which adds an RDF statement:

IES_TOOL.add_triple(
    subject=my_person.uri,
    predicate='http://ies.data.gov.uk/ontology/ies4#hasCharacteristic',
    obj=characteristic_uri
)

Namespaces

To bind an RDF namespace prefix, we need to 'register' that prefix. The library pre-configures prefixes: xsd:, dc:, rdf:, rdfs:, owl:, ies:, iso8601:, iso3166:

Registering these prefixes just enables shorter, more readable RDF to be produced. The methods in the IES tool itself all require fully expanded URIs.

from ies_tool.ies_tool import IES_TOOL


IES_TOOL.add_prefix("data:", "http://example.com/rdf/testdata#")

As a default http://example.com/rdf/testdata# is used as a default data namespace. This can be changed:

IES_TOOL.uri_stub = 'http://mydomain.org/rdf-data#'

Note this will also set the blank prefix : to http://mydomain.org/rdf-data#

Saving/creating RDF

As a text string

my_rdf_string = IES_TOOL.get_rdf(format="turtle") 

As the IES tool uses RDFLib by default. If another storage plug-in has been used, it may not support all the RDF bindings.

Saving RDF locally

IES_TOOL.save_rdf("path/to/my/file.ttl", rdf_format="ttl")  

To clear the graph:

IES_TOOL.clear_graph()