What is a Knowledge Graph for Integral Ecology?

We live in an age of overwhelming information, but ecological knowledge, wisdom, and action often remain fragmented, buried in reports, scattered across languages, or locked in formats only specialists can access.

That’s where the Digital Library of Integral Ecology comes in. Our mission is to bring this information together, from scientific papers to NGO reports to faith-based reflections, into a single, interconnected digital library that helps researchers, educators, and communities act for the common good. One of the tools that helps us discovery and interrogate integral ecology is a knowledge graph. This codebase and set of tutorials will walk you through the technologies, opportunities, and tradeoffs in building this feature of the Digitial Library of Integral Ecology.

What Do We Mean by "Integral Ecology"?

The term integral ecology comes from Laudato Si’, Pope Francis' encyclical on the environment. It’s about recognizing the deep connections between ecological, social, cultural, and spiritual concerns. Climate change, deforestation, loss of biodiversity — these aren’t just technical problems. They are moral ones, economic ones, and spiritual ones too.

Integral ecology asks us to think in systems, and to see how everything is connected.

And What’s a Knowledge Graph?

A knowledge graph is a way of storing and exploring knowledge by looking at relationships. Imagine a big web:

• A report mentions “Amazon rainforest”
• It connects to a location
• The report is published by UNESCO
• It discusses concepts like resilience and biodiversity
• It cites other documents that are connected too

All of this is represented not just as flat text, but as linked data — relationships between concepts, people, places, and ideas. This is what lets us ask better questions and see deeper patterns.

What We're Building

We are creating a system that:

1. Extracts text from ecological reports and scientific papers (even in PDF format)
2. Identifies key concepts, organizations, places, species, and ideas — in multiple languages
3. Links them together in a searchable, visual graph (using Neo4j)
4. Lets people annotate and refine that knowledge with simple tools
5. Trains smarter AI models over time that understand ecology more deeply

Why It Matters

• Researchers can discover connections across disciplines and languages
• NGOs can map their work to broader systems and goals
• Educators can explore real-world ecological examples interactively
• Communities can build shared understanding of their bioregions

We believe this is a project not just of technology — but of ecological conversion.

What’s Next?

In the next post, we’ll walk through the building blocks of the graph: documents, entities, and relationships, and how we transform text into structure.