This article presents a novel Proxy-Pointer architecture designed to improve entity and relationship reconciliation within large knowledge graphs. As enterprise knowledge graphs expand, maintaining them becomes increasingly complex due to inconsistent data governance and evolving business rules. Traditional Retrieval-Augmented Generation (RAG) methods are inadequate for effectively handling these challenges, as they often lead to fragmented document sections devoid of necessary context. The Proxy-Pointer approach utilizes vector matches as pointers to retrieve intact document sections, allowing for more accurate entity extraction and reducing the computational burden on the knowledge graph. By implementing five zero-cost engineering techniques, this method enhances the efficiency and accuracy of knowledge graph updates and entity reconciliation processes.
Proxy-Pointer RAG: Enhancing Entity and Relationship Management in Large Knowledge Graphs
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