Knowledge-Graph-Building-From-Scratch

This repository contains a Jupyter Notebook (test.ipynb) that provides a practical guide to understanding and building a Knowledge Graph (KG) from unstructured text data using Natural Language Processing (NLP) techniques.

What is a Knowledge Graph?

A Knowledge Graph is a structured representation of information that describes interlinked entities and their relationships. At its core, a KG consists of:

• Nodes (Entities): Representing real-world objects, concepts, or events (e.g., "Germany," "Holy Roman Empire," "1815").
• Edges (Relationships): Representing the connections or associations between entities (e.g., "began in," "inhabited," "formed in").

The smallest unit in a Knowledge Graph is often referred to as a "triple," which comprises two entities connected by a single relationship (e.g., (Germanic tribes, inhabited, region)).

Why Build a Knowledge Graph?

Knowledge Graphs are powerful tools for:

• Enhanced Search: Providing more relevant and contextual search results.
• Data Integration: Connecting disparate data sources.
• AI Applications: Powering intelligent applications like recommendation systems, chatbots, and question-answering systems.
• Semantic Understanding: Enabling machines to understand the meaning and relationships within data.

How to Build a Knowledge Graph from Text

The test.ipynb notebook demonstrates a step-by-step approach to extracting information from text and transforming it into a structured Knowledge Graph. This process primarily leverages various NLP techniques:

1. Sentence Segmentation: Breaking down raw text into individual sentences.
2. Dependency Parsing: Analyzing the grammatical structure of sentences to identify relationships between words.
3. Parts of Speech (POS) Tagging: Identifying the grammatical role of each word (e.g., noun, verb, adjective).
4. Entity Recognition: Identifying and classifying key entities within the text.

The notebook provides practical examples and Python code using popular NLP libraries to illustrate these concepts.

Notebook Highlights

• Introduction to KGs: Clear definitions of nodes, edges, and triples.
• NLP for KG Construction: Explanation of essential NLP techniques like dependency parsing, POS tagging, and entity recognition.
• Code Examples: Practical Python code snippets demonstrating how to implement these techniques.
• SpaCy Integration: Utilizes the spaCy library for efficient linguistic processing.
• Relationship Extraction: Illustrates how to extract meaningful relationships between entities from text.

Getting Started

To run the notebook and explore the code:

1. Clone this repository:

   git clone [YOUR_REPOSITORY_URL]

2. Navigate to the repository directory:

   cd [YOUR_REPOSITORY_NAME]

3. Install the necessary libraries (e.g., spaCy, pandas):

   pip install -r requirements.txt
   python -m spacy download en_core_web_sm

4. Open the test.ipynb notebook using Jupyter Lab or Jupyter Notebook:

   jupyter lab test.ipynb

   or

   jupyter notebook test.ipynb

Feel free to explore the code, experiment with different texts, and adapt it for your own Knowledge Graph projects!

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