  Large Language Models (LLMs) have exhibited impressive proficiency in various
natural language processing (NLP) tasks, which involve increasingly complex
reasoning. Knowledge reasoning, a primary type of reasoning, aims at deriving
new knowledge from existing one.While it has been widely studied in the context
of knowledge graphs (KGs), knowledge reasoning in LLMs remains underexplored.
In this paper, we introduce Chain-of-Knowledge, a comprehensive framework for
knowledge reasoning, including methodologies for both dataset construction and
model learning. For dataset construction, we create KnowReason via rule mining
on KGs. For model learning, we observe rule overfitting induced by naive
training. Hence, we enhance CoK with a trial-and-error mechanism that simulates
the human process of internal knowledge exploration. We conduct extensive
experiments with KnowReason. Our results show the effectiveness of CoK in
refining LLMs in not only knowledge reasoning, but also general reasoning
benchmarkms.
