Knowledge Graph Generator in Google Colab

A comprehensive knowledge graph generator using LLM. A tool that extracts key entities and their relationships from text and visualizes them in an interactive knowledge graph directly in a Google Colab notebook using PyVis.

Table of Contents

• Applications
• Features
• Setup & Usage
• Project Structure

Applications

1. Information Retrieval and Organization

• Data Management: Organize large volumes of textual data into structured, interconnected entities. Useful for creating databases or enhancing existing ones.
• Content Summarization: Summarize key information from long documents or articles by extracting main entities and their relationships.

2. Education

• Teaching Aid: Assist educators in creating interactive teaching materials by visually representing complex subjects and their interrelations.
• Student Projects: Provide a tool for students to visualize and present their research or project findings.

3. Knowledge Discovery

• Research: Aid researchers in identifying relationships between different concepts, facilitating new insights and hypothesis generation.
• Literature Reviews: Summarize findings from numerous studies by mapping out key terms and their connections.

Features

• Text Analysis: Extracts entities and their relationships from input text.
• Interactive Visualization: Generates and displays a knowledge graph using PyVis.
• Colab Integration: Fully integrated into Google Colab for easy use and visualization.

Setup & Usage

• Open KGG.ipynb file on google colab and import requirements.txt file in google colab.
• Run all the cells.

Project Structure

knowledge-graph-generator/
│
├── README.md                     # This readme file
├── KGG.ipynb  # Example Colab notebook
└── requirements.txt              # List of dependencies