How to Build a Knowledge Graph using Python and Spacy

Information Extraction is a process of extracting information in a more structured way i.e., the information which is machine-understandable. It consists of sub fields which cannot be easily solved. Therefore, an approach to store data in a structured manner is Knowledge Graph which is a set of three-item sets called Triple where the set combines a subject, a predicate and an object.In this article, we will discuss how to build a knowledge graph using Python and Spacy.

Let’s get started.

Code Implementation

Import all the libraries required for this project.

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

These hubs will be the elements that are available in Wikipedia. Edges are the connections interfacing these elements to each other. We will extricate these components in an unaided way, i.e., we will utilize the punctuation of the sentences.

The primary thought is to experience a sentence and concentrate the subject and the item as and when they are experienced. First, we need to pass the text to the function. The text will be broken down and place each token or word in a category. After we have arrived at the finish of a sentence, we clear up the whitespaces which may have remained and afterwards we’re all set, we have gotten a triple. For example in the statement “Bhubaneswar is categorised as a Tier-2 city” it will give a triple focusing on the main subject(Bhubaneswar, categorised, Tier-2 city).

Below we have defined the code to get triples that can be used to build knowledge graphs.

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()

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Buddha Community

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

…

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

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Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

• md5
• sha1
• sha224, sha256, sha384 and sha512

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