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Create a Knowledge Graph from Text

In this project, we’ll learn how to preprocess text data and use it to create a knowledge graph.

Create a Knowledge Graph from Text

You will learn to:

Preprocess the text by finding patterns.

Use spaCy to resolve coreferences.

Perform named entity recognition.

Create and visualize a knowledge graph using the NetworkX library.


Natural Language Processing

Text Preprocessing

Knowledge Graphs


Basic understanding of text preprocessing

Familiarity with NLP concepts

Understanding of named entity recognition

Familiarity with graphs


spaCy logo



NetworkX logo



Project Description

A knowledge graph is widely used to concentrate knowledge in a compact and cohesive form for easy retrieval. Creating a knowledge graph involves getting data, preprocessing it, converting words to their root forms, and extracting subject-predicate-object triple.

In this project, we’ll use The Wikipedia Library to get English language sentences and pass them through every step listed above to create a knowledge graph at the end. The graph will also facilitate information extraction via simple queries. All the text processing will be done via the open-source NLP library, spaCy. The network creation and information extraction will be completed using the NetworkX library, and lastly, the pyvis library will be used to display the knowledge graph.

Project Tasks


Getting Started

Task 0: Get Started

Task 1: Import Libraries


Data Preparation

Task 2: Load the Data

Task 3: Preprocess the Data


Application of NLP Techniques

Task 4: Recognize Named Entities

Task 5: Compute Coreference Clusters

Task 6: Resolve Coreferences

Task 7: Extract Relationships


The Knowledge Graph

Task 8: Create a Graph

Task 9: List the Related Entities