GeoILP: A Synthetic Dataset to Guide Large-Scale Rule Induction
Si Chen Richong Zhang Xu Zhang
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.
dataset, inductive logic programming, rule induction, neural-symbolic methods