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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 0.0. Visualizing Metadata
RDF (Resource Description Framework) is a data model for information on the internet. It can be used to describe just about anything, but is usually applied to bibliographic collections
Step1:  Correct Zotero RDF
Zotero isn't exactly a pro at creating valid RDF/XML. The code cell below fixes a known issue with Zotero RDF documents.
Step2:  Parse RDF
We use the rdflib Python package to parse the corrected RDF document. The code-cell below creates an empty RDF graph, and then reads the triples from the corrected RDF document created above.
Step3:  Create a GraphML file
GraphML is a popular graph serialization format. My favorite graph visualization tool, Cytoscape, can read GraphML. The NetworkX Python package makes it easy to create GraphML files.
Step4:  The code-cell below will create a new GraphML file that we can import in Cytoscape.
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Python Code:
import rdflib
import networkx as nx
import os
rdf_path = 'data/example.rdf'
Explanation: 0.0. Visualizing Metadata
RDF (Resource Description Framework) is a data model for information on the internet. It can be used to describe just about anything, but is usually applied to bibliographic collections: representing metadata about published documents. 
RDF XML is a grammar/serialization format for representing RDF. There are other ways of representing RDF, like N-triples, Turtle, and JSON-LD.
One of the core concepts of RDF is representing (meta)data as a graph. Every element of the RDF document (a file containing RDF statements) is a node in the graph: articles, people, journals, literals (like volume numbers), etc. These nodes are called resources. Resources are linked together in triples: tri-partite statements consisting of a subject, a predicate, and an object.
In this notebook, we'll convert a simple Zotero RDF/XML document into a GraphML graph (a graph serialization format) and visualize that graph using Cytoscape.
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Note   This exercise is intended only to introduce you to RDF and graphs, and isn't something that you are likely to do as part of an analysis. There is a sample RDF/XML file included in the data subdirectory, describing a single document. Use that file to start. You can try this with your own RDF if you want, but even a moderate number of documents will lead to extremely large and unweildy graphs. So, be careful.
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End of explanation
with open(rdf_path, 'r') as f:
    corrected = f.read().replace('rdf:resource rdf:resource',
                                 'link:link rdf:resource')
# The corrected graph will be saved to a file with `_corrected` 
#  added to the name. E.g. if the original RDF document was 
#  called `example.rdf`, the new file will be called 
#  `example_corrected.rdf`.
base, name = os.path.split(rdf_path)
corrected_name = '%s_corrected_.%s' % tuple(name.split('.'))
corrected_rdf_path = os.path.join(base, corrected_name)
with open(corrected_rdf_path, 'w') as f:
    f.write(corrected)
Explanation: Correct Zotero RDF
Zotero isn't exactly a pro at creating valid RDF/XML. The code cell below fixes a known issue with Zotero RDF documents.
End of explanation
rdf_graph = rdflib.Graph()
rdf_graph.load(corrected_rdf_path)
Explanation: Parse RDF
We use the rdflib Python package to parse the corrected RDF document. The code-cell below creates an empty RDF graph, and then reads the triples from the corrected RDF document created above.
End of explanation
graph = nx.DiGraph()    # Metadata is `directed`.
for s, p, o in rdf_graph.triples((None, None, None)):
    # The .toPython() method converts rdflib objects into objects
    #  that any Python module can understand (e.g. str, int, float).
    graph.add_edge(s.toPython(), 
                   o.toPython(), 
                   attr_dict={'predicate': p.toPython()})
print 'Added %i nodes and %i edges to the graph' % (graph.order(),
                                                    graph.size())
Explanation: Create a GraphML file
GraphML is a popular graph serialization format. My favorite graph visualization tool, Cytoscape, can read GraphML. The NetworkX Python package makes it easy to create GraphML files.
End of explanation
graphml_path = 'output/example.graphml'
nx.write_graphml(graph, graphml_path)
Explanation: The code-cell below will create a new GraphML file that we can import in Cytoscape.
End of explanation
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