KnowledgeGraph GPT
Convert text to knowledge graph, export as JSON, access open-source code on GitHub.
What is KnowledgeGraph GPT?
KnowledgeGraph GPT takes raw text and converts it into structured knowledge graphs, making it easier to visualise relationships between concepts, entities, and ideas. The tool outputs your graphs as JSON, which you can integrate into other applications or analyse further. Because the code is open-source and available on GitHub, you can inspect how it works, modify it for your needs, or self-host it rather than relying on a third-party service. This makes it useful for anyone working with text analysis, research organisation, or building knowledge management systems without licensing restrictions.
Key Features
Text to knowledge graph conversion
automatically extracts entities and relationships from unstructured text
JSON export
download your knowledge graph in a standard format for use in other tools or systems
Open-source code
full access to the source code on GitHub for transparency and customisation
No authentication required
get started immediately without creating an account
Web-based interface
use it directly in your browser without installation
Pros & Cons
Advantages
- Completely free and open-source, with no subscription fees or usage limits
- Transparent codebase means you can verify exactly how your data is being processed
- JSON output is flexible and compatible with most data processing workflows
- Self-hosting option available if you need to keep data on your own infrastructure
Limitations
- Limited documentation or support resources compared to commercial tools
- May struggle with complex, ambiguous, or domain-specific text without customisation
- No built-in visualisation interface; you'll need external tools to display graphs visually
Use Cases
Extracting structured data from research papers or documents for literature reviews
Building knowledge bases from unstructured business documentation
Mapping relationships in interview transcripts or qualitative research data
Creating entity and relationship datasets for training machine learning models
Organising technical documentation into queryable knowledge structures