Logical rules are essential for uncovering the logical connections between
relations, which could improve the 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
the 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, a rule validator harnesses the
reasoning ability of LLMs to validate the logical correctness of ranked rules
through chain-of-thought reasoning. 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.

利用大型语言模型 (ChatRule) 在知识图谱中挖掘逻辑规则，包括基于语义和结构信息的规则生成器以及利用现有知识图谱评估和验证规则的模块，展示了方法的有效性和可扩展性。

ChatRule: 大语言模型挖掘逻辑规则进行知识图推理

ChatRule: Mining Logical Rules with Large Language Models for Knowledge  Graph Reasoning

We introduce the General Video Game Rule Generation problem, and the
eponymous software framework which will be used in a new track of the General
Video Game AI (GVGAI) competition. The problem is, given a game level as input,
to generate the rules of a game that fits that level. This can be seen as the
inverse of the General Video Game Level Generation problem. Conceptualizing
these two problems as separate helps breaking the very hard problem of
generating complete games into smaller, more manageable subproblems. The
proposed framework builds on the GVGAI software and thus asks the rule
generator for rules defined in the Video Game Description Language. We describe
the API, and three different rule generators: a random, a constructive and a
search-based generator. Early results indicate that the constructive generator
generates playable and somewhat interesting game rules but has a limited
expressive range, whereas the search-based generator generates remarkably
diverse rulesets, but with an uneven quality.

我们介绍了 GVGAI 竞赛的新领域 ——General Video Game Rule Generation 问题，该问题的解决方案是使用基于 GVGAI 软件的软件框架，给定一个游戏关卡，生成符合该关卡的游戏规则，我们描述了 API 和三种不同的规则生成器：随机生成器、构造性生成器和基于搜索的生成器，并且初步结果表明，构造性生成器生成有趣的游戏规则，但表达范围有限，而基于搜索的生成器可以生成多样化的游戏规则，但质量参差不齐。