BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question
  Answering
Knowledge graph question answering (KGQA) presents significant challenges due
to the structural and semantic variations across input graphs. Existing works
rely on Large Language Model (LLM) agents for graph traversal and retrieval; an
approach that is sensitive to traversal initialization, as it is prone to
entity linking errors and may not generalize well to custom ("bring-your-own")
KGs. We introduce BYOKG-RAG, a framework that enhances KGQA by synergistically
combining LLMs with specialized graph retrieval tools. In BYOKG-RAG, LLMs
generate critical graph artifacts (question entities, candidate answers,
reasoning paths, and OpenCypher queries), and graph tools link these artifacts
to the KG and retrieve relevant graph context. The retrieved context enables
the LLM to iteratively refine its graph linking and retrieval, before final
answer generation. By retrieving context from different graph tools, BYOKG-RAG
offers a more general and robust solution for QA over custom KGs. Through
experiments on five benchmarks spanning diverse KG types, we demonstrate that
BYOKG-RAG outperforms the second-best graph retrieval method by 4.5% points
while showing better generalization to custom KGs. BYOKG-RAG framework is
open-sourced at https://github.com/awslabs/graphrag-toolkit.
68780165ff8f47a7f8644396
Costas Mavromatis
68780165ff8f47a7f8644397
Soji Adeshina
68780165ff8f47a7f8644398
Vassilis N. Ioannidis
68780165ff8f47a7f8644399
Zhen Han
68780165ff8f47a7f864439a
Qi Zhu
68780165ff8f47a7f864439b
Ian Robinson
68780165ff8f47a7f864439c
Bryan Thompson
68780165ff8f47a7f864439d
Huzefa Rangwala
68780165ff8f47a7f864439e
George Karypis
BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question
  Answering
Knowledge graph question answering (KGQA) presents significant challenges due
to the structural and semantic variations across input graphs. Existing works
rely on Large Language Model (LLM) agents for graph traversal and retrieval; an
approach that is sensitive to traversal initialization, as it is prone to
entity linking errors and may not generalize well to custom ("bring-your-own")
KGs. We introduce BYOKG-RAG, a framework that enhances KGQA by synergistically
combining LLMs with specialized graph retrieval tools. In BYOKG-RAG, LLMs
generate critical graph artifacts (question entities, candidate answers,
reasoning paths, and OpenCypher queries), and graph tools link these artifacts
to the KG and retrieve relevant graph context. The retrieved context enables
the LLM to iteratively refine its graph linking and retrieval, before final
answer generation. By retrieving context from different graph tools, BYOKG-RAG
offers a more general and robust solution for QA over custom KGs. Through
experiments on five benchmarks spanning diverse KG types, we demonstrate that
BYOKG-RAG outperforms the second-best graph retrieval method by 4.5% points
while showing better generalization to custom KGs. BYOKG-RAG framework is
open-sourced at https://github.com/awslabs/graphrag-toolkit.
BYOKG-RAG combines LLMs with specialized graph retrieval tools to enhance KGQA, improving generalization and performance over custom knowledge graphs.
Large Language Model (LLM) graph traversal entity linking BYOKG-RAG graph retrieval tools question entities candidate answers reasoning paths OpenCypher queries graph linking graph context iterative refinement custom KGs generalization