Old Wine in a New Bottle: How HippoRAG Revolutionizes Retrieval with Knowledge Graphs

In the ever-evolving world of AI and language models, it’s easy to get caught up in chasing the latest and greatest innovations. However, sometimes the most effective solutions come from combining tried-and-true methods with modern approaches. Enter HippoRAG, a novel technique that breathes new life into a 20-year-old algorithm to enhance retrieval-augmented generation (RAG) systems.

HippoRAG, introduced in a recent paper from Ohio State University, tackles a common problem in vanilla RAG systems: the loss of connections between different chunks of text during the embedding process. By integrating knowledge graphs with RAG, HippoRAG preserves these vital relationships and improves the quality of retrieved context.

Source: HippoRAG Paper

The secret sauce of HippoRAG lies in its use of the Personalized PageRank (PPR) algorithm, a variation of the famous PageRank algorithm developed by Google co-founder Larry Page in 2000. This approach allows for efficient traversal of the knowledge graph without relying on traditional graph databases like Neo4j or complex query languages.

Here’s how HippoRAG works:

1. Offline Indexing:

• Extract noun phrases and relationships from the corpus using LLMs