  Retrieval-augmented generation (RAG) has significantly advanced large
language models (LLMs) by enabling dynamic information retrieval to mitigate
knowledge gaps and hallucinations in generated content. However, these systems
often falter with complex reasoning and consistency across diverse queries. In
this work, we present Think-on-Graph 2.0, an enhanced RAG framework that aligns
questions with the knowledge graph and uses it as a navigational tool, which
deepens and refines the RAG paradigm for information collection and
integration. The KG-guided navigation fosters deep and long-range associations
to uphold logical consistency and optimize the scope of retrieval for precision
and interoperability. In conjunction, factual consistency can be better ensured
through semantic similarity guided by precise directives. ToG${2.0}$ not only
improves the accuracy and reliability of LLMs' responses but also demonstrates
the potential of hybrid structured knowledge systems to significantly advance
LLM reasoning, aligning it closer to human-like performance. We conducted
extensive experiments on four public datasets to demonstrate the advantages of
our method compared to the baseline.
