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="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:skos="http://www.w3.org/2008/05/skos#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:owlxml="http://www.w3.org/2006/12/owl2-xml#">
<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="http://vocab.nerc.ac.uk/collection/L19/current/005/"/>
<skos:broader rdf:resource="http://vocab.nerc.ac.uk/collection/P05/current/014/"/>
<skos:narrower rdf:resource="http://vocab.nerc.ac.uk/collection/P01/current/ACBSADCP/"/>
<skos:related rdf:resource="http://my.fake.domain/test2"/>
</skos:Concept>
<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:member>
<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"/>
</skos:Concept>
</skos:member>
</skos:Collection>
</rdf:RDF>"""
>>> 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()
['http://my.fake.domain/test1',
'http://my.fake.domain/test2',
'http://my.fake.domain/collection']
>>> loader.values()
[<Concept('http://my.fake.domain/test1')>,
<Concept('http://my.fake.domain/test2')>,
<Collection('http://my.fake.domain/collection')>]
>>> len(loader)
3
>>> concept = loader['http://my.fake.domain/test1']
>>> concept
<Concept('http://my.fake.domain/test1')>
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!
INFO:skos:parsing http://vocab.nerc.ac.uk/collection/L19/current/005/
INFO:skos:parsing http://vocab.nerc.ac.uk/collection/P05/current/014/
INFO:skos:parsing http://vocab.nerc.ac.uk/collection/P01/current/ACBSADCP/
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
['http://my.fake.domain/test1',
'http://my.fake.domain/test2',
'http://my.fake.domain/collection']
>>> concept = loader['http://my.fake.domain/test1']
>>> concept.synonyms # other objects are still correctly referenced by the object model
{'http://vocab.nerc.ac.uk/collection/L19/current/005/': <Concept('http://vocab.nerc.ac.uk/collection/L19/current/005/')>}
>>> 'http://vocab.nerc.ac.uk/collection/L19/current/005/' in loader # but are not referenced directly
False
>>> loader.flat = True # flatten the structure so *all* objects are directly referenced
>>> loader.keys() # lists all 6 objects
['http://vocab.nerc.ac.uk/collection/P05/current/014/',
'http://vocab.nerc.ac.uk/collection/L19/current/005/',
'http://my.fake.domain/collection',
'http://my.fake.domain/test1',
'http://my.fake.domain/test2',
'http://vocab.nerc.ac.uk/collection/P01/current/ACBSADCP/']
>>> 'http://vocab.nerc.ac.uk/collection/L19/current/005/' in loader
True
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
{'http://vocab.nerc.ac.uk/collection/L19/current/005/': <Concept('http://vocab.nerc.ac.uk/collection/L19/current/005/')>,
'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['http://vocab.nerc.ac.uk/collection/P05/current/014/'].narrower
Concept instances also provide easy access to the other SKOS data:
>>> concept.uri
'http://my.fake.domain/test1'
>>> 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
'http://my.fake.domain/collection'
>>> collection.title
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']
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
<Collection('http://my.fake.domain/collection')>
>>> # get all concepts that have the string 'water' in them:
>>> session2.query(skos.Concept).filter(skos.Concept.prefLabel.ilike('%water%')).all()
[<Concept('http://my.fake.domain/test1')>,
<Concept('http://vocab.nerc.ac.uk/collection/P01/current/ACBSADCP/')>]
- Python == 2.{6,7}
- SQLAlchemy SQLAlchemy >= 0.7.5
- RDFLib >= 2.4.2
- iso8601plus
- unittest2 if running the tests with Python < 2.7
Current and previous versions of the software are available at http://github.com/geo-data/python-skos/tags and http://pypi.python.org/pypi/python-skos.
Download and unpack the source, then run the following from the root distribution directory:
python setup.py install
It is recommended that you also run:
python setup.py test
This exercises the comprehensive package test suite.
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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.
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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.