Retrieval-Augmented Generation (RAG) has revolutionized how AI systems access information, but it still struggles with context limitations and factual hallucinations. Graph RAG represents an advanced approach that leverages structured knowledge graphs to ground AI responses in factual relationships. At Babelscape, we have been experimenting with this technology on our multilingual knowledge graph WordAtlas (and others!) for quite some time now, with the aim of demonstrating how Graph RAG can transform AI understanding across languages.
What is Graph RAG?
Graph RAG enhances traditional retrieval by replacing (or complementing) simple vector similarity search with structured knowledge graph traversal. While conventional RAG treats documents as isolated chunks, Graph RAG understands the rich web of relationships between entities, concepts, and facts. Consider our example of “Where is The Name of the Rose set?”: a traditional system might return text chunks containing both terms. Graph RAG, however, navigates a knowledge structure that explicitly connects “The Name of the Rose” to “Umberto Eco” through an “AUTHOR” relationship, while also understanding that it’s set in a “Benedictine abbey” during the “14th century”. This network of connections provides context that simple text retrieval cannot match. This structured approach allows for precise navigation through complex information landscapes, following conceptual relationships rather than just statistical similarity between text fragments.
Why Graph RAG Matters
Enhanced Grounding: By anchoring information retrieval in explicit knowledge structures, Graph RAG dramatically reduces hallucinations. AI systems can follow verified paths between concepts rather than generating connections that might not exist.
Multilingual Understanding: Graph RAG implementations can excel across languages when built on multilingual knowledge structures. A query about “Don Quixote” in English can retrieve information originally stored in Spanish, Italian, or dozens of other languages, maintaining semantic relationships regardless of the source language.
Contextual Richness: Knowledge graphs capture nuanced relationships that flat text cannot. When retrieving information about “Cervantes,” a Graph RAG system understands his role as an author, his Spanish origin, and connections to his works—providing AI with a much more complete picture for generating relevant responses.
Implementing Graph RAG with WordAtlas
At Babelscape, we’ve implemented our own Graph RAG on WordAtlas, our multilingual knowledge graph evolved from BabelNet. This implementation showcases how a rich knowledge graph with millions of concepts and relationships across 600 languages can serve as an excellent foundation for Graph RAG systems.
The key advantage in our implementation lies in the semantic structure-concepts connected through typed relationships that AI systems can traverse with confidence, ensuring that “AUTHOR” means the same thing whether expressed in English, Mandarin, or Arabic.
While WordAtlas serves as our implementation example, Graph RAG can be adapted to work with various knowledge graphs, tailored to specific domains and business needs. The core principles of structured knowledge traversal remain the same regardless of the underlying graph.
Looking Forward
As AI systems become increasingly integrated into global business operations, the need for factually grounded, multilingual knowledge retrieval will only grow. Graph RAG represents an important evolutionary step in making AI systems not just more knowledgeable, but more trustworthy.
At Babelscape, we’re applying these advanced techniques to solve real business challenges across language barriers, combining cutting-edge NLP research with practical applications.
Interested in exploring how Graph RAG approaches can enhance your AI applications?
Connect with us to learn more about potential solutions
and partnership opportunities.