Author(s): Yogendra Pal
Originally published on Towards AI.
Leveraging graph-based retrieval beneath vector embeddings to add structure, depth, and accuracy to RAG pipelines
Communities in a knowledge graph are groups of entities that are more strongly connected to each other than to the rest of the graph. Microsoft’s research sheds light on how these communities capture meaningful real-world groupings – such as people working on the same project, related historical figures, or concepts that appear together in documents. By summarizing each community, the graph gains an additional semantic layer: it is not simply stored fact And edgesIt also encodes collective context. This makes graphs more interpretable, reduces noise, and improves downstream reasoning because the system can reason not only about individual nodes, but also about the higher-level structures they form.
The article discusses Microsoft’s research on knowledge graphs, emphasizing the importance 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 questions. Various technical strategies such as layered retrieval, entity extraction and community detection are explored using Neo4j to show how structured knowledge can bring better accuracy and transparency to AI models, ultimately benefiting tasks that involve reasoning about interconnected data.
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Published via Towards AI
