Reranking documents based on graph representations of the documents
Topics: Graph RAG, Knowledge Graph, LLM Readability, LLMO / GEO, Probably in use, Ranking, Reranking
This patent by Google describes a method for improving how a search or question-answering system ranks retrieved documents. When a user submits a query, the system retrieves a set of documents and then builds two types of graphs: first, an Abstract Meaning Representation (AMR) graph that captures the semantic meaning and concepts within documents, and second, a document graph that maps connections between documents based on shared concepts from the AMR graph. Using graph neural networks (GNNs), the system reranks the documents by identifying cross-document relationships — including those that are not immediately obvious — and then feeds the top-ranked documents into a large language model to generate a final answer. The approach is designed to outperform existing reranking methods while using fewer computational resources.
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