Comparison of Architectural Differences
- knowledge organization: GraphRAG is based on semantic ternary storage, traditional RAG relies on vector databases
- Query precisionSupport SPARQL complex relational query, breaking through the limitations of keyword matching.
- reasoning ability: Multi-hop reasoning using path discovery with knowledge graphs
Practical effect enhancement
In an enterprise FAQ system test: 1) long-tail question answering accuracy increased from 58% to 82%; 2) recall rate of related questions increased by 3 times; 3) support for queries requiring relational reasoning, such as "Which products are compatible with X battery models?" and other queries that require relational reasoning.
Realization points
To use it, you need to 1) ensure the quality of ternary extraction; 2) configure a suitable SPARQL template; and 3) set up a pipeline for post-processing the results. The system automatically generates theoutput.ttlfile can be used directly for GraphRAG initialization.
This answer comes from the articleOntoCast: an intelligent framework for extracting semantic triples from documentsThe
May not be reproduced without permission:AI productivity tools " What are the advantages of OntoCast's GraphRAG feature over traditional RAG?
