Co-Occurring Tag Analysis

Analysing how tags co-occur across various Parliamentary publications. The idea behind this is to see whether there are naturally occurring groupings of topic tags by virtue of their co-occurence when used to tag different classes of Parlimanetary publication.

In [6]:
#Data files
!ls data/dataexport
edms        proceedings terms

Utils

Import a library that lets us work with the data files:

In [11]:
#Data is provided as Turtle/ttl files - rdflib handles those

#!pip3 install rdflib
from rdflib import Graph

Simple utility to load all the .ttl files in a particular directory into a graph:

In [110]:
import os
def ttl_graphbuilder(path,g=None,debug=False):
    if g is None:
        g=Graph()
    for ttl in [f for f in os.listdir(path) if f.endswith('.ttl')]:
        if debug: print(ttl)
        g.parse('{}/{}'.format(path,ttl), format='turtle')
    return g

Tools for running queries over a graph and either printing the result or putting it into a pandas dataframe:

In [61]:
def rdfQuery(graph,q):
    ans=graph.query(q)
    for row in ans:
        for el in row:
            print(el,end=" ")
        print()

#ish via https://github.com/schemaorg/schemaorg/blob/sdo-callisto/scripts/dashboard.ipynb
import pandas as pd
def sparql2df(graph,q, cast_to_numeric=True):
    a=graph.query(q)
    c = []
    for b in a.bindings:
        rowvals=[]
        for k in a.vars:
            rowvals.append(b[k])
        c.append(rowvals)

    df = pd.DataFrame(c)
    df.columns = [str(v) for v in a.vars]
    if cast_to_numeric:
        df = df.apply(lambda x: pd.to_numeric(x, errors='ignore'))

    return df

Tools to support the export and display of graphs - netowrkx package is handy in this respect, eg exporting to GEXF format for use with Gephi.

In [ ]:
import networkx as nx

Exploring the Data - Terms

In [42]:
path='data/dataexport/terms'
termgraph=ttl_graphbuilder(path)
In [91]:
#What's in the graph generally?
q='''
SELECT DISTINCT ?x ?y ?z {
    ?x ?y ?z.
} LIMIT 10
'''
rdfQuery(termgraph,q)
http://data.parliament.uk/terms/93225 http://www.w3.org/2004/02/skos/core#narrower http://data.parliament.uk/terms/90383 
http://data.parliament.uk/terms/414110 http://www.w3.org/2004/02/skos/core#notation  
http://data.parliament.uk/terms/408942 http://www.w3.org/2004/02/skos/core#related http://data.parliament.uk/terms/60315 
http://data.parliament.uk/terms/357565 http://www.w3.org/2004/02/skos/core#narrower http://data.parliament.uk/terms/349840 
http://data.parliament.uk/terms/60278 http://www.w3.org/2004/02/skos/core#exactMatch http://data.parliament.uk/terms/97318 
http://data.parliament.uk/terms/37000 http://www.w3.org/2004/02/skos/core#notation ORG 
http://data.parliament.uk/terms/1326 http://www.w3.org/2004/02/skos/core#broader http://data.parliament.uk/terms/1 
http://data.parliament.uk/terms/83361 http://www.w3.org/2004/02/skos/core#prefLabel Standing Veterinary Committee 
http://data.parliament.uk/terms/300910 http://www.w3.org/2004/02/skos/core#exactMatch http://data.parliament.uk/terms/313904 
http://data.parliament.uk/terms/431592 http://www.w3.org/2004/02/skos/core#broader http://data.parliament.uk/terms/1 
In [92]:
#What does a term have associated with it more specicfically?
q='''
SELECT DISTINCT ?y ?z {
    <http://data.parliament.uk/terms/95551> ?y ?z.
} LIMIT 10
'''
rdfQuery(termgraph,q)
http://www.w3.org/2004/02/skos/core#narrower http://data.parliament.uk/terms/95502 
http://www.w3.org/2004/02/skos/core#narrower http://data.parliament.uk/terms/95494 
http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2004/02/skos/core#Concept 
http://www.w3.org/2004/02/skos/core#notation TPG 
http://www.w3.org/2004/02/skos/core#narrower http://data.parliament.uk/terms/95550 
http://www.w3.org/2004/02/skos/core#broader http://data.parliament.uk/terms/95548 
http://www.w3.org/2004/02/skos/core#prefLabel Defence policy 
http://www.w3.org/2004/02/skos/core#narrower http://data.parliament.uk/terms/95586 

Looks like the label is what we want:

In [97]:
q='''
SELECT DISTINCT ?z ?topic {
    ?z <http://www.w3.org/2004/02/skos/core#prefLabel> ?topic.
} LIMIT 10
'''
sparql2df(termgraph,q)
Out[97]:
z topic
0 http://data.parliament.uk/terms/90385 Broadcasting programmes
1 http://data.parliament.uk/terms/4491 British Airports Group
2 http://data.parliament.uk/terms/28888 Derbyshire Royal Infirmary NHS Trust
3 http://data.parliament.uk/terms/24830 Citizens Charter Complaints Task Force
4 http://data.parliament.uk/terms/84469 UK Energy Research Centre
5 http://data.parliament.uk/terms/36872 Graduate School of Business Administration
6 http://data.parliament.uk/terms/65884 Railway Group
7 http://data.parliament.uk/terms/67412 Safety-Net Foundation
8 http://data.parliament.uk/terms/83361 Standing Veterinary Committee
9 http://data.parliament.uk/terms/50788 Probation and aftercare

Exploring the Data - EDMS

In [87]:
path='data/dataexport/edms'
g=ttl_graphbuilder(path)
In [88]:
#See what's there generally...
q='''
SELECT DISTINCT ?x ?y ?z {
    ?x ?y ?z.
} LIMIT 10
'''
rdfQuery(g,q)
http://data.parliament.uk/edms/50212 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://data.parliament.uk/schema/parl#EarlyDayMotion 
http://data.parliament.uk/edms/50457 http://data.parliament.uk/schema/parl#motionText That this House notes the announcement of 300 redundancies at the Nestlé manufacturing factories in York, Fawdon, Halifax and Girvan and that production of the Blue Riband bar will be transferred to Poland; acknowledges in the first three months of 2017 Nestlé achieved £21 billion in sales, a 0.4 per cent increase over the same period in 2016; further notes 156 of these job losses will be in York, a city that in the last six months has seen 2,000 job losses announced and has become the most inequitable city outside of the South East, and a further 110 jobs from Fawdon, Newcastle; recognises the losses come within a month of triggering Article 50, and as negotiations with the EU on the UK leaving the EU and the UK's future with the EU are commencing; further recognises the cost of importing products, including sugar, cocoa and production machinery, has risen due to the weakness of the pound and the uncertainty over the UK's future relationship with the single market and customs union; and calls on the Government to intervene and work with hon. Members, trades unions GMB and Unite and the company to avert these job losses now and prevent further job losses across Nestlé. 
http://data.parliament.uk/edms/49408 http://data.parliament.uk/schema/parl#topic http://data.parliament.uk/terms/95650 
http://data.parliament.uk/edms/49467 http://data.parliament.uk/schema/parl#topic http://data.parliament.uk/terms/95676 
http://data.parliament.uk/edms/49667 http://data.parliament.uk/schema/parl#topic http://data.parliament.uk/terms/95758 
http://data.parliament.uk/edms/50446 http://purl.org/dc/terms/date 2017-04-24T23:00:00+00:00 
http://data.parliament.uk/edms/49677 http://purl.org/dc/terms/date 2016-09-06T23:00:00+00:00 
http://data.parliament.uk/edms/50210 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://data.parliament.uk/schema/parl#EarlyDayMotion 
http://data.parliament.uk/edms/49500 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://data.parliament.uk/schema/parl#EarlyDayMotion 
http://data.parliament.uk/edms/49973 http://data.parliament.uk/schema/parl#topic http://data.parliament.uk/terms/95518 
In [90]:
#Explore a specific EDM
q='''
SELECT DISTINCT ?y ?z {
    <http://data.parliament.uk/edms/50457> ?y ?z.
}
'''
rdfQuery(g,q)
http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://data.parliament.uk/schema/parl#EarlyDayMotion 
http://data.parliament.uk/schema/parl#motionText That this House notes the announcement of 300 redundancies at the Nestlé manufacturing factories in York, Fawdon, Halifax and Girvan and that production of the Blue Riband bar will be transferred to Poland; acknowledges in the first three months of 2017 Nestlé achieved £21 billion in sales, a 0.4 per cent increase over the same period in 2016; further notes 156 of these job losses will be in York, a city that in the last six months has seen 2,000 job losses announced and has become the most inequitable city outside of the South East, and a further 110 jobs from Fawdon, Newcastle; recognises the losses come within a month of triggering Article 50, and as negotiations with the EU on the UK leaving the EU and the UK's future with the EU are commencing; further recognises the cost of importing products, including sugar, cocoa and production machinery, has risen due to the weakness of the pound and the uncertainty over the UK's future relationship with the single market and customs union; and calls on the Government to intervene and work with hon. Members, trades unions GMB and Unite and the company to avert these job losses now and prevent further job losses across Nestlé. 
http://purl.org/dc/terms/date 2017-04-25T23:00:00+00:00 
http://data.parliament.uk/schema/parl#topic http://data.parliament.uk/terms/95591 
http://data.parliament.uk/schema/parl#topic http://data.parliament.uk/terms/95530 

Let's merge the EDM graph data with the terms data.

In [98]:
path='data/dataexport/edms'
g=ttl_graphbuilder(path,termgraph)

Now we can look at the term labels associated with a particular EDM.

In [99]:
q='''
SELECT DISTINCT ?t ?z {
    <http://data.parliament.uk/edms/50114> <http://data.parliament.uk/schema/parl#topic> ?z.
    ?z <http://www.w3.org/2004/02/skos/core#prefLabel> ?t.
} LIMIT 10
'''
rdfQuery(g,q)
Arms control http://data.parliament.uk/terms/95494 
Defence policy http://data.parliament.uk/terms/95551 
International politics and government http://data.parliament.uk/terms/95650 
North America http://data.parliament.uk/terms/95690 

We can also create a table that links topic labels with EDMs.

In [79]:
q='''
SELECT DISTINCT ?edms ?topic {
    ?edms <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://data.parliament.uk/schema/parl#EarlyDayMotion>.
    ?edms <http://data.parliament.uk/schema/parl#topic> ?z.
    ?z <http://www.w3.org/2004/02/skos/core#prefLabel> ?topic.
}
'''
g_df=sparql2df(g,q)
g_df.head()
Out[79]:
edms topic
0 http://data.parliament.uk/edms/50424 Sports and Olympic Games
1 http://data.parliament.uk/edms/50424 Children and families
2 http://data.parliament.uk/edms/50129 Adult education
3 http://data.parliament.uk/edms/49551 Sports and Olympic Games
4 http://data.parliament.uk/edms/50276 Sports and Olympic Games

From this table, we can a generate a bipartite networkx graph that links topic labels with EDMs.

In [80]:
nxg=nx.from_pandas_dataframe(g_df, 'edms', 'topic')
#nx.write_gexf(nxg,'edms.gexf')

We can then project this bipartite graph onto just the topic label nodes - edges will now connect nodes that are linked through one or more common EDMs.

In [81]:
from networkx.algorithms import bipartite
#We can find the sets of names/tags associated with the disjoint sets in the graph
edms,topic=bipartite.sets(nxg)

#Collapse the bipartite graph to a graph of topic lables connected via a common EDM
topicgraph= bipartite.projected_graph(nxg, topic)
nx.write_gexf(topicgraph,'edms_topics.gexf')

We can also generate a weighted graph, where edges are weighted relative to how many times topics are linked through different EDMs.

In [101]:
topicgraph_weighted= bipartite.weighted_projected_graph(nxg, topic)
nx.write_gexf(topicgraph_weighted,'edms_topics_weighted.gexf')

Exploring the Data - proceedings

In [111]:
path='data/dataexport/proceedings'
p=ttl_graphbuilder(path,debug=True)
0006D323-D0B5-4E22-A26E-75ABB621F58E.ttl
Traceback (most recent call last):

  File "/usr/local/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)

  File "<ipython-input-111-e3cc91dfc0eb>", line 2, in <module>
    p=ttl_graphbuilder(path,debug=True)

  File "<ipython-input-110-9b9078d48c95>", line 7, in ttl_graphbuilder
    g.parse('{}/{}'.format(path,ttl), format='turtle')

  File "/usr/local/lib/python3.6/site-packages/rdflib/graph.py", line 1043, in parse
    parser.parse(source, self, **args)

  File "/usr/local/lib/python3.6/site-packages/rdflib/plugins/parsers/notation3.py", line 1870, in parse
    p.loadStream(source.getByteStream())

  File "/usr/local/lib/python3.6/site-packages/rdflib/plugins/parsers/notation3.py", line 434, in loadStream
    return self.loadBuf(stream.read())    # Not ideal

  File "/usr/local/lib/python3.6/site-packages/rdflib/plugins/parsers/notation3.py", line 440, in loadBuf
    self.feed(buf)

  File "/usr/local/lib/python3.6/site-packages/rdflib/plugins/parsers/notation3.py", line 466, in feed
    i = self.directiveOrStatement(s, j)

  File "/usr/local/lib/python3.6/site-packages/rdflib/plugins/parsers/notation3.py", line 487, in directiveOrStatement
    j = self.statement(argstr, i)

  File "/usr/local/lib/python3.6/site-packages/rdflib/plugins/parsers/notation3.py", line 725, in statement
    j = self.property_list(argstr, i, r[0])

  File "/usr/local/lib/python3.6/site-packages/rdflib/plugins/parsers/notation3.py", line 1086, in property_list
    i = self.objectList(argstr, j, objs)

  File "/usr/local/lib/python3.6/site-packages/rdflib/plugins/parsers/notation3.py", line 1134, in objectList
    i = self.object(argstr, i, res)

  File "/usr/local/lib/python3.6/site-packages/rdflib/plugins/parsers/notation3.py", line 1406, in object
    j = self.subject(argstr, i, res)

  File "/usr/local/lib/python3.6/site-packages/rdflib/plugins/parsers/notation3.py", line 733, in subject
    return self.item(argstr, i, res)

  File "/usr/local/lib/python3.6/site-packages/rdflib/plugins/parsers/notation3.py", line 829, in item
    return self.path(argstr, i, res)

  File "/usr/local/lib/python3.6/site-packages/rdflib/plugins/parsers/notation3.py", line 837, in path
    j = self.nodeOrLiteral(argstr, i, res)

  File "/usr/local/lib/python3.6/site-packages/rdflib/plugins/parsers/notation3.py", line 1472, in nodeOrLiteral
    j, s = self.strconst(argstr, i, delim)

  File "/usr/local/lib/python3.6/site-packages/rdflib/plugins/parsers/notation3.py", line 1565, in strconst
    "newline found in string literal")

  File "<string>", line unknown
BadSyntax
In [113]:
!ls {path}
0006D323-D0B5-4E22-A26E-75ABB621F58E.ttl
00133F82-F04E-4F1E-AE6C-CC5B6720E072.ttl
00329467-EFFF-4C18-B4C7-D6511C43C146.ttl
00DA951D-1DF2-4DAF-ABDE-CBFFD2958838.ttl
01A4E161-A86D-4A22-BAB2-AEC9E07A19C6.ttl
02239CBB-E462-439D-853A-98D0D8156C60.ttl
024D61E2-09CB-4055-8E88-3F842A27FBF1.ttl
02C8BCC1-6BF4-4B49-8D8E-D3B6589DB26B.ttl
02DB9D81-010A-436E-8E00-7E3F5CAC95E1.ttl
02ED21B0-A2A6-4161-919C-3B143EE887D0.ttl
02F5D2BC-B031-40E2-B918-56C126FDCED1.ttl
03556065-507A-4B4E-A824-66DD1E96E105.ttl
0372A8D2-E0DB-4FF8-889D-4981676A943C.ttl
0382E657-48C5-457A-81E3-2CF5C522FCE9.ttl
03BFC240-9C9F-43E1-A04F-DA604D52A79E.ttl
03E1F7D7-CD11-4E54-829B-4FA67DE210F3.ttl
0401820A-2896-4F56-BB87-759B20CF834D.ttl
0439FDFB-2EB6-4014-9789-7826216C2410.ttl
049A4CE6-CC57-411C-9507-1F86D6EB1024.ttl
04B908B8-D29F-4CA2-A760-F346A325A817.ttl
04FF79AD-B0C2-44D1-8A1C-EA7C770D155B.ttl
0535AD72-6401-41BD-911B-706C3BDD17C4.ttl
059C4CD3-B876-41A4-8ACD-046109416476.ttl
05EBDFD7-C789-41C9-8F3B-61F9E5B806E0.ttl
064415AF-EC2A-41D0-8A13-0ABA5318CA7A.ttl
07780C78-5204-47EB-B339-9CCEE2129077.ttl
077D30E7-4A38-4B6D-9708-A2BCE971FC42.ttl
078837E6-3DDF-4FC9-AB84-08192917B9F0.ttl
07B89A35-3D1B-4D4E-8F28-E22DC9DFC2C9.ttl
07F9C2A8-031D-4A59-9C72-981885B7D6E7.ttl
0936937D-8DD1-4B79-9DF8-53417492491B.ttl
093A9D10-68F0-478F-A947-946B9A11823B.ttl
09550611-8605-41AB-89DA-08C4F82A8E11.ttl
096E2854-4771-4023-AC89-496BBFF65B5D.ttl
09C0E2EA-B8E2-4443-BDD1-EC56C625E564.ttl
0A065836-8978-4F9E-94D0-479421331F8A.ttl
0A1EEF00-31B0-47C9-9554-348686C7563D.ttl
0A46EDB4-4A0F-4CD2-A72B-99E76D14528B.ttl
0A4AFAB7-A39A-44FB-BD15-14B29FCAFFE6.ttl
0A542677-3FE1-4FF3-8035-17F6E5A964E6.ttl
0A97A886-8621-4C9F-B053-584E54E2D69C.ttl
0ABD2BAF-E90E-47C4-BA15-0C8A98059612.ttl
0AD1729E-E7CC-489B-9048-DDC0C20E2777.ttl
0B114054-9126-4942-918A-D0733028E74B.ttl
0B257E86-7A03-4B2E-8EE5-4304614533DF.ttl
0B68CDFA-B18D-45C9-A637-DFC3EFB813AF.ttl
0B815504-6EE6-45C9-9854-85FD2A390A76.ttl
0B8F8427-907F-4E98-8FC8-8179CFAF1C5F.ttl
0BAF2BEB-510F-41B9-94B7-81D7C9715F13.ttl
0C3F1385-D6DB-4DAD-B492-E99DE0D2064C.ttl
0CDBF1EA-7122-40EA-82C5-8922F2C5D390.ttl
0CDEBC9C-990D-4846-ACC4-420AD4F3E51A.ttl
0D2198F8-D4EA-4965-B16E-30EEB6ADEBAF.ttl
0D291850-E72E-4DA8-9311-8FBD5C8C8CEC.ttl
0D85E1B5-5C7E-49ED-83BE-8865D5BDD380.ttl
0D898C87-DE94-4B1E-B3B9-5BB2EBBF1DAE.ttl
0DB335B5-5CF5-4200-B4F5-650D6DD1FE1F.ttl
0DF20776-17E4-474D-8A68-F84FE70F15EA.ttl
0E7173BA-C93C-4B53-9428-A441AEA0990A.ttl
0EB2E459-5184-41F9-97CC-16D0A00B4480.ttl
0EDD2EA5-5E6C-419B-A11E-5ADC9A4A4B24.ttl
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0F8CB188-8A94-43CC-B0DE-D36BE6354054.ttl
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10A00BB1-6FD3-473C-8D43-F81FC33071FF.ttl
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119D03B2-28E7-4537-B3F7-30328EF22157.ttl
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160E7239-27EB-4074-AA28-8164AD76ECDC.ttl
16276464-6091-431A-ACA4-BF25C6DF4B27.ttl
1630F651-AD7D-494B-BC08-5C47AF675D24.ttl
16829ED1-5EAD-460E-8E3E-A521B983F048.ttl
16CA2DF0-4123-45EB-B89F-1954CF655C18.ttl
172163FF-52C3-488E-9880-63FD42229640.ttl
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191D7D37-3AE6-484F-A81C-8BA0CAB3D7D7.ttl
193E9531-F5B8-4911-A808-3D62D62D876F.ttl
19585142-D09B-4D8F-839A-B0CB215AF054.ttl
198FD9FC-CB33-48CA-A0B6-03A220AD70C7.ttl
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1AD88318-4F1B-4DF9-A31B-38F36956734F.ttl
1B49E760-5CB1-4106-BFD8-25B173658062.ttl
1BE3B464-62EE-49FE-A83E-DF6B27ABA98C.ttl
1C8F8D6D-37B6-4327-88C8-27E489E18A7E.ttl
1CBE4575-3381-4968-BE69-74C351DAB1B3.ttl
1CD84796-9882-4CC6-A60E-380EB84A3658.ttl
1D1FB7CB-577F-495B-BF26-926C86848CFD.ttl
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