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HyperRAG: From Broken Triples to Complete Relational Reasoning
If you’ve ever built a RAG system that looked smart in demos but got strangely lost on multi-hop questions, have you considered this idea: For multi-hop RAG failures, the bottleneck may lie not only in retrieval quality, but also in how complex facts are represented before retrieval begins.
The Bottleneck of Combining RAG with Knowledge Graphs
Most GraphRAG systems today are built on top of binary knowledge graphs. Knowledge is broken down into simple triples: head entity, relation, tail entity. This representation is widely used, but its simplicity comes at a cost.
Two structural limitations stand out.
First is semantic fragmentation. Many real-world facts involve multiple entities and roles within a single relational event. When those facts are decomposed into isolated binary triples, part of the original semantic structure is lost.
Figure 1 uses an n-ary fact roughly of the form: “Bruce Seth Green, Sam Weisman, Sam Pillsbury, and Eric Laneuville directed TV 101 in English in California.”
In a binary graph, this single holistic fact must be decomposed into multiple pairwise triples, which breaks apart its original semantic unity. The connection between director, movie, and…
