Do graph databases outperform embeddings for relational retrieval tasks?

This explores whether graph databases outperform embeddings for relational retrieval — and the corpus reframes the question: it's less 'which wins' and more 'which fits the query.' Embeddings retrieve by similarity, which is probabilistic and great for fuzzy semantic matching but blind to relationships. When a question requires following chains of connections — multi-hop traversal, aggregate counts, 'how is A linked to B through C' — graph databases win because they replace probabilistic similarity with deterministic traversal you can express in a query language like Cypher When do graph databases outperform vector embeddings for retrieval?. The cost is real, though: building and maintaining a graph is expensive, so the advantage shows up in relational, enterprise-style domains, not everywhere.

The sharper insight is that the framing of 'embeddings vs. graphs' may itself be the wrong altitude. One line of work argues retrieval failures are architectural, not tunable — embeddings measure association rather than relevance, and there are mathematical limits on how many distinct documents a fixed embedding dimension can even represent Where do retrieval systems fail and why?. That's why the most interesting answer isn't 'pick graphs' but 'route the query.' Systems that classify what a query needs and send it to the matching structure — a table for aggregation, a graph for relations, plain chunks for lookup — outperform any single uniform retrieval method, grounded in cognitive-fit theory: match the representation to the task Can routing queries to task-matched structures improve RAG reasoning?.

Graphs also aren't one thing. The expensive part everyone complains about — pre-building a corpus-wide graph that goes stale — can be sidestepped by constructing a small logic graph from the query itself at inference time, keeping multi-hop reasoning without the maintenance burden Can query-time graph construction replace pre-built knowledge graphs?. And ordinary pairwise graphs can't capture relations that bind three or more entities at once; hypergraphs preserve those joint constraints across multi-step reasoning that flat lists and binary edges lose Can hypergraphs capture multi-hop reasoning better than graphs?. Hierarchy adds another dimension — multimodal knowledge graphs over whole books answer cross-chapter, global questions that flat chunk retrieval simply cannot reach Can multimodal knowledge graphs answer questions that flat retrieval cannot?, echoing a broader finding that separating query planning from answer synthesis beats flat architectures on complex queries Do hierarchical retrieval architectures outperform flat ones on complex queries?.

What you might not expect: structure doesn't only help at retrieval time — it can teach the model. Fine-tuning on reasoning tasks derived from medical knowledge-graph paths produced state-of-the-art performance across fifteen domains, suggesting that the compositional structure of a graph matters more than raw scale Can knowledge graphs teach models deep domain expertise?. So the real takeaway is that graphs and embeddings aren't competitors so much as different tools: embeddings for semantic reach, graphs for relational precision — and the systems that win are the ones that know which question they're holding.