  Rules have a number of desirable properties. It is easy to understand, infer
new knowledge, and communicate with other inference systems. One weakness of
the previous rule induction systems is that they only find rules within a
knowledge base (KB) and therefore cannot generalize to more open and complex
real-world rules. Recently, the language model (LM)-based rule generation are
proposed to enhance the expressive power of the rules. In this paper, we
revisit the differences between KB-based rule induction and LM-based rule
generation. We argue that, while KB-based methods inducted rules by discovering
data commonalities, the current LM-based methods are "learning rules from
rules". This limits these methods to only produce "canned" rules whose patterns
are constrained by the annotated rules, while discarding the rich expressive
power of LMs for free text.
  Therefore, in this paper, we propose the open rule induction problem, which
aims to induce open rules utilizing the knowledge in LMs. Besides, we propose
the Orion (\underline{o}pen \underline{r}ule \underline{i}nducti\underline{on})
system to automatically mine open rules from LMs without supervision of
annotated rules. We conducted extensive experiments to verify the quality and
quantity of the inducted open rules. Surprisingly, when applying the open rules
in downstream tasks (i.e. relation extraction), these automatically inducted
rules even outperformed the manually annotated rules.
