# Induction of Interpretable Possibilistic Logic Theories from Relational Data

## TL;DR

This paper introduces a new method for learning interpretable relational models using possibilistic logic, which results in faster, more transparent theories compared to traditional Markov Logic Networks.

## Contribution

The paper presents a novel SRL approach that encodes relational models as stratified classical theories with explicit certainty levels, enhancing interpretability and efficiency.

## Key findings

- Faster learning compared to Markov Logic Networks
- Produces more interpretable models
- Explicit encoding of certainty levels in theories

## Abstract

The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kind of weighted logical formulas, which make them considerably more interpretable than those obtained by e.g. neural networks. In practice, however, these models are often still difficult to interpret correctly, as they can contain many formulas that interact in non-trivial ways and weights do not always have an intuitive meaning. To address this, we propose a new SRL method which uses possibilistic logic to encode relational models. Learned models are then essentially stratified classical theories, which explicitly encode what can be derived with a given level of certainty. Compared to Markov Logic Networks (MLNs), our method is faster and produces considerably more interpretable models.