KAG (Knowledge Augmented Generation)
Knowledge-augmented generation framework built on the OpenSPG knowledge graph engine that translates natural-language questions into logical forms over a schema-constrained KG and combines retrieval, KG reasoning, and numerical calculation in a hybrid reasoning engine.
Description
KAG (Knowledge Augmented Generation, arXiv 2409.13731, ACM Web Conf 2025) is Ant Group's logical-form-guided RAG framework, built on the OpenSPG knowledge graph engine. KAG attacks the noise problem of OpenIE-based approaches (HippoRAG, GraphRAG) by using a schema-constrained KG with semantic alignment. It introduces mutual indexing between KG nodes and source chunks, and a hybrid reasoning engine that orchestrates four operator types — retrieval, KG reasoning, language reasoning, numerical calculation — by translating NL questions into logical forms. Published numbers significantly outperform NaiveRAG and HippoRAG on multi-hop QA (HotpotQA F1 +19.6%, 2Wiki +33.5%).
Solution
Offline: domain schema is defined in SPG, sources are processed into a schema-constrained KG with chunk-level mutual indexing. Online: the user question is translated to a logical form by the planner; the logical form orchestrates a sequence of operator calls (retrieval, KG reasoning, language reasoning, numerical calculation); operator outputs feed the generator. The bidirectional KG↔chunk index lets graph traversal pull supporting evidence from text.
Primary use cases
- professional-domain QA where schema-constrained knowledge is available (legal, medical, finance)
- multi-hop reasoning over KG + text with quantifiable accuracy gains over RAG and HippoRAG
- decision-support systems where rigorous fact-grounding is required
- knowledge-graph construction with semantic alignment to reduce OpenIE noise
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