Author(s): Yogender Pal

Originally published on Towards AI.

Leveraging graph-based retrieval underneath vector embeddings to add structure, depth, and accuracy to RAG pipelines

Communities in a knowledge graph are clusters of entities that are more strongly connected to each other than to the rest of the graph. Microsoft’s research highlights how these communities capture meaningful real-world groupings — such as people working on the same project, related historical figures, or concepts appearing together across documents. By summarizing each community, the graph gains an additional semantic layer: it doesn’t just store facts and edges, it also encodes collective context. This makes the graph more interpretable, reduces noise, and improves downstream reasoning because the system can reason not only about individual nodes, but about the higher-level structures they form.

The article discusses Microsoft’s research on knowledge graphs, emphasizing the significance of community structures within them. It explains how communities can enhance local search by providing summarized information about entities and their relationships, thereby improving contextual understanding for complex queries. Various technical strategies such as layered retrieval, entity extraction, and community detection using Neo4j are explored to showcase how structured knowledge can lead to better accuracy and transparency in AI models, ultimately benefiting tasks that involve reasoning about interconnected data.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI