GeoILP: A Synthetic Dataset to Guide Large-Scale Rule Induction

Si Chen, Richong Zhang, Xu Zhang

Keywords: dataset, inductive logic programming, rule induction, neuro-symbolic methods

Abstract:

Inductive logic programming (ILP) is a machine learning approach aiming to learn explanatory rules from data.
While existing ILP systems can successfully solve small-scale tasks, large-scale applications with various language biases are rarely explored.
Besides, it is crucial for a large majority of current ILP systems to require expert-defined language bias, which hampers the development of ILP towards broader utilizations.
In this paper, we introduce GeoILP, a large-scale synthetic dataset of diverse ILP tasks involving numerous aspects of language bias.
These tasks are built from geometry problems, at the level from textbook exercise to regional International Mathematical Olympiad (IMO), with the help of a deduction engine.
These problems are elaborately selected to cover all challenging language biases, such as recursion, predicate invention, and high arity.
Experimental results show that no existing method can solve GeoILP tasks.
In addition, along with classic symbolic-form data, we provide image-form data to boost the development of the joint learning of neural perception and symbolic rule induction.

Primary Area: datasets and benchmarks

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 9564