From Retrieval to Reasoning: Enhancing HippoRAG with Graph-Based Semantics

How an ontology-based knowledge graph boosts the multi-hop Q&A accuracy of one of the leading schemaless GraphRAG systems Retrieval Augmented Generation (RAG) has become the standard for grounding large language models (LLMs) in proprietary data. However, as we push RAG systems into more complex domains, we see that standard RAG and GraphRAG solutions are not sufficient. Standard RAG relies heavily on vector similarity search. While efficient, it suffers from a fundamental “tunnel vision”: it retrieves chunks based on semantic proximity to the query, but often fails to “conn..