knowledge graph

import spacy
from spacy.lang.en import English
import networkx as nx
import matplotlib.pyplot as plt

def getSentences(text):
    nlp = English()
    nlp.add_pipe(nlp.create_pipe('sentencizer'))
    document = nlp(text)
    return [sent.string.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 = ''
    relation = ''
    subjectConstruction = ''
    objectConstruction = ''
    for token in tokens:
        printToken(token)
        if "punct" in token.dep_:
            continue
        if isRelationCandidate(token):
            relation = appendChunk(relation, token.lemma_)
        if isConstructionCandidate(token):
            if subjectConstruction:
                subjectConstruction = appendChunk(subjectConstruction, token.text)
            if objectConstruction:
                objectConstruction = appendChunk(objectConstruction, token.text)
        if "subj" in token.dep_:
            subject = appendChunk(subject, token.text)
            subject = appendChunk(subjectConstruction, subject)
            subjectConstruction = ''
        if "obj" in token.dep_:
            object = appendChunk(object, token.text)
            object = appendChunk(objectConstruction, object)
            objectConstruction = ''
    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:
        G.add_node(triple[0])
        G.add_node(triple[1])
        G.add_node(triple[2])
        G.add_edge(triple[0], triple[1])
        G.add_edge(triple[1], triple[2])
    pos = nx.spring_layout(G)
    plt.figure(figsize=(12, 8))
    nx.draw(G, pos, edge_color='black', width=1, linewidths=1,
            node_size=500, node_color='skyblue', alpha=0.9,
            labels={node: node for node in G.nodes()})
    plt.axis('off')
    plt.show()

if __name__ == "__main__":
    text = "Bhubaneswar is the capital and largest city of the Indian state of Odisha. The city is bounded by the Daya River " \
            "to the south and the Kuakhai River to the east; the Chandaka Wildlife Sanctuary "\
            "and Nandankanan Zoo lie in the western and northern parts of Bhubaneswar." \
            "Bhubaneswar is categorised as a Tier-2 city." \
            "Bhubaneswar and Cuttack are often referred to as the 'twin cities of Odisha'. " \
            "The city has a population of 1163000."        
    sentences = getSentences(text)
    nlp_model = spacy.load('en_core_web_sm')
    triples = []
    print (text)
    for sentence in sentences:
        triples.append(processSentence(sentence))
    printGraph(triples)