Update spicy library to 3.1.0 and added some instructions

README.md

@@ -1,3 +1,14 @@
 # python-knowledge-graph
 A Python and scaCy implementation of a basic Knowledge Graph.
 See more details here in this blog post: https://programmerbackpack.com/python-nlp-tutorial-information-extraction-and-knowledge-graphs/
+
+## Python library setup
+
+Run the following commands
+
+`pip3 install -r requirements.txt`
+
+`python3 -m spacy download en_core_web_sm`
+
+## Start the program
+`python3 knowledgegraph.py`

knowledgegraph.py

@@ -3,26 +3,32 @@
 import networkx as nx
 import matplotlib.pyplot as plt
 
+
 def getSentences(text):
     nlp = English()
-    nlp.add_pipe(nlp.create_pipe('sentencizer'))
+    nlp.add_pipe('sentencizer')
     document = nlp(text)
-    return [sent.string.strip() for sent in document.sents]
+    return [str(sent).strip() for sent in document.sents]
+
 
 def printToken(token):
     print(token.text, "->", token.dep_)
 
+
 def appendChunk(original, chunk):
     return original + ' ' + chunk
 
+
 def isRelationCandidate(token):
     deps = ["ROOT", "adj", "attr", "agent", "amod"]
     return any(subs in token.dep_ for subs in deps)
 
+
 def isConstructionCandidate(token):
     deps = ["compound", "prep", "conj", "mod"]
     return any(subs in token.dep_ for subs in deps)
 
+
 def processSubjectObjectPairs(tokens):
     subject = ''
     object = ''
@@ -49,13 +55,15 @@ def processSubjectObjectPairs(tokens):
             object = appendChunk(objectConstruction, object)
             objectConstruction = ''
 
-    print (subject.strip(), ",", relation.strip(), ",", object.strip())
+    print(subject.strip(), ",", relation.strip(), ",", object.strip())
     return (subject.strip(), relation.strip(), object.strip())
 
+
 def processSentence(sentence):
     tokens = nlp_model(sentence)
     return processSubjectObjectPairs(tokens)
 
+
 def printGraph(triples):
     G = nx.Graph()
     for triple in triples:
@@ -73,6 +81,7 @@ def printGraph(triples):
     plt.axis('off')
     plt.show()
 
+
 if __name__ == "__main__":
 
     text = "London is the capital and largest city of England and the United Kingdom. Standing on the River " \
@@ -91,7 +100,7 @@ def printGraph(triples):
     nlp_model = spacy.load('en_core_web_sm')
 
     triples = []
-    print (text)
+    print(text)
     for sentence in sentences:
         triples.append(processSentence(sentence))
 

requirements.txt

@@ -0,0 +1,3 @@
+spacy==3.1.0
+matplotlib==3.4.2
+networkx==2.5.1