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A basic implementation of some core elements of the SKOS object model
tag: v0.0.1

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Python SKOS


This package provides a basic implementation of some of the core elements of the SKOS object model, as well as an API for loading SKOS XML resources. See the SKOS Primer for an introduction to SKOS.

The object model builds on SQLAlchemy to provide persistence and querying of the object model from within a SQL database.


Firstly, the package supports Python's logging module which can provide useful feedback about various module actions so let's activate it:

>>> import logging
>>> logging.basicConfig(level=logging.INFO)

The package reads graphs generated by the rdflib library so let's parse a (rather contrived) SKOS XML file into a graph:

>>> import rdflib
>>> xml = """<?xml version="1.0"?>
<rdf:RDF xmlns:rdf="" xmlns:skos="" xmlns:dc="" xmlns:owlxml="">
  <skos:Concept rdf:about="http://my.fake.domain/test1">
    <skos:prefLabel>Acoustic backscatter in the water column</skos:prefLabel>
    <skos:definition>Includes all parameters covering the strength of acoustic signal return, including absolute measurements of returning signal strength as well as parameters expressed as backscatter (the proportion of transmitted signal returned)</skos:definition>
    <owlxml:sameAs rdf:resource=""/>
    <skos:broader rdf:resource=""/>
    <skos:narrower rdf:resource=""/>
    <skos:related rdf:resource="http://my.fake.domain/test2"/>
  <skos:Collection rdf:about="http://my.fake.domain/collection">
    <dc:title>Test Collection</dc:title>
    <dc:description>A collection of concepts used as a test</dc:description>
    <skos:member rdf:resource="http://my.fake.domain/test1"/>
      <skos:Concept rdf:about="http://my.fake.domain/test2">
        <skos:prefLabel>Another test concept</skos:prefLabel>
        <skos:definition>Just another concept</skos:definition>
        <skos:related rdf:resource="http://my.fake.domain/test1"/>
>>> graph = rdflib.Graph()
>>> graph.parse(data=xml, format="application/rdf+xml")

Now we can can use the skos.RDFLoader object to access the SKOS data as Python objects:

>>> import skos
>>> loader = skos.RDFLoader(graph)

This implements a mapping interface:

>>> loader.keys()
>>> loader.values()
>>> len(loader)
>>> concept = loader['http://my.fake.domain/test1']
>>> concept

As well as some convenience methods returning mappings of specific types:

>>> loader.getConcepts()
{'http://my.fake.domain/test1': <Concept('http://my.fake.domain/test1')>,
 'http://my.fake.domain/test2': <Concept('http://my.fake.domain/test2')>}
>>> loader.getCollections()
{'http://my.fake.domain/collection': <Collection('http://my.fake.domain/collection')>}
>>> loader.getConceptSchemes() # we haven't got any `ConceptScheme`s

The RDFLoader constructor also takes a max_depth parameter which defaults to 0. This parameter determines the depth to which RDF resources are resolved i.e. it is used to limit the depth to which links are recursively followed. E.g. the following will allow one level of external resources to be parsed and resolved:

>>> loader = skos.RDFLoader(graph, max_depth=1) # you need to be online for this!

If you want to resolve an entire (and potentially very large!) graph then use max_depth=float('inf').

Another constructor parameter is the boolean flag flat. This can also be toggled post-instantiation using the RDFLoader.flat property. When set to False (the default) only SKOS objects present in the inital graph are directly referenced by the loader: objects created indirectly by parsing other resources will only be referenced by the top level objects:

>>> loader.keys() # lists 3 objects
>>> concept = loader['http://my.fake.domain/test1']
>>> concept.synonyms # other objects are still correctly referenced by the object model
{'': <Concept('')>}
>>> '' in loader # but are not referenced directly
>>> loader.flat = True # flatten the structure so *all* objects are directly referenced
>>> loader.keys() # lists all 6 objects
>>> '' in loader

The Concept.synonyms demonstrated above shows the container (an instance of skos.Concepts) into which skos::exactMatch and owlxml::sameAs references are placed. The skos.Concepts container class is a mapping that is mutable via the set-like add and discard methods, as well responding to del on keys:

>>> synonym = skos.Concept('test3', 'a synonym for test1', 'a definition')
>>> concept.synonyms.add(synonym)
>>> concept.synonyms
{'': <Concept('')>,
 'test3': <Concept('test3')>}
>>> del concept.synonyms['test3'] # or...
>>> concept.synonyms.discard(synonym)

Similar to Concept.synonyms Concept.broader, Concept.narrower and Concept.related are all instances of skos.Concepts:

>>> assert concept in concept.broader[''].narrower

Concept instances also provide easy access to the other SKOS data:

>>> concept.uri
>>> concept.prefLabel
'Acoustic backscatter in the water column'
>>> concept.definition
'Includes all parameters covering the strength of acoustic signal return, including absolute measurements of returning signal strength as well as parameters expressed as backscatter (the proportion of transmitted signal returned)'

Access to the ConceptScheme and Collection objects to which a concept belongs is also available via the Concept.schemes and Concept.collections properties respectively:

>>> concept.collections
{'http://my.fake.domain/collection': <Collection('http://my.fake.domain/collection')>}
>>> collection = concept.collections['http://my.fake.domain/collection']
>>> assert concept in collection.members

As well as the Collection.members property, Collection instances provide access to the other SKOS data:

>>> collection.uri
>>> collection.title
>>> collection.description
'A collection of concepts used as a test'

Collection.members is a skos.Concepts instance, so new members can added and removed using the skos.Concepts interface:

>>> collection.members.add(synonym)
>>> del collection.members['test3']

Integrating with SQLAlchemy

python-skos has been designed to be integrated with the SQLAlchemy ORM when required. This provides scalable data persistence and querying capabilities. The following example uses an in-memory SQLite database to provide a taste of what is possible. Explore the SQLAlchemy ORM documentation to build on this, using alternative databases and querying techniques...

>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite:///:memory:') # the in-memory database
>>> from sqlalchemy.orm import sessionmaker
>>> Session = sessionmaker(bind=engine)
>>> session1 = Session() # get a session handle on the database
>>> skos.Base.metadata.create_all(session1.connection()) # create the required database schema
>>> session1.add_all(loader.values()) # add all the skos objects to the database
>>> session1.commit() # commit these changes

>>> session2 = Session() # a new database session, created somewhere else ;)
>>> session2.query(skos.Collection).first() # obtain our one and only collection
>>> # get all concepts that have the string 'water' in them:
>>> session2.query(skos.Concept).filter(skos.Concept.prefLabel.ilike('%water%')).all()



Current and previous versions of the software are available at and


Download and unpack the source, then run the following from the root distribution directory:

python install

It is recommended that you also run:

python test

This exercises the comprehensive package test suite.


  • Only part of the more recent SKOS core object model is supported. Extending the code to support more of the SKOS specification should not be difficult, however.

  • This is a new and immature package: please treat it as beta quality software and report any bugs in the github issue tracker. Having said that it sees production use in the MEDIN Portal and MEDIN metadata tool software.

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