Interactive Model Refinement in Relational Domains with Inductive Logic Programming

This paper presents an interactive system for exploring and editing logic-based machine learning models specialised for the relational reasoning problem domain. Prior work has highlighted the value of visual interfaces for enabling effective user interaction during model training. However, these existing systems require two-dimensional tabular data and are not well-suited to relational machine learning tasks. Logic-based methods, such as those developed in the field of Inductive Logic Programming, can address this; they retain relational information by operating directly on raw relational data while remaining inherently interpretable and editable to allow for human intervention. However, such systems require logical expertise to operate effectively and do not enable visual exploration. We aim to address this; taking design inspiration from equivalent interfaces for propositional learning, we present a visual interface that enhances the usability of inductive logic programming systems for domain experts without a background in computational logic.