  Logical rules are essential for uncovering the logical connections between
relations, which could improve reasoning performance and provide interpretable
results on knowledge graphs (KGs). Although there have been many efforts to
mine meaningful logical rules over KGs, existing methods suffer from
computationally intensive searches over the rule space and a lack of
scalability for large-scale KGs. Besides, they often ignore the semantics of
relations which is crucial for uncovering logical connections. Recently, large
language models (LLMs) have shown impressive performance in the field of
natural language processing and various applications, owing to their emergent
ability and generalizability. In this paper, we propose a novel framework,
ChatRule, unleashing the power of large language models for mining logical
rules over knowledge graphs. Specifically, the framework is initiated with an
LLM-based rule generator, leveraging both the semantic and structural
information of KGs to prompt LLMs to generate logical rules. To refine the
generated rules, a rule ranking module estimates the rule quality by
incorporating facts from existing KGs. Last, the ranked rules can be used to
conduct reasoning over KGs. ChatRule is evaluated on four large-scale KGs,
w.r.t. different rule quality metrics and downstream tasks, showing the
effectiveness and scalability of our method.
