# Non-Parametric Learning of Gaifman Models

arXiv: https://arxiv.org/abs/2001.00528 · HTML: https://mlsanity.com/paper/2001.00528/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2020.

## Built on by (most cited first, 1 of 1)

- [Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits](https://mlsanity.com/paper/2106.13895/) (2021)

## Abstract (arXiv)

We consider the problem of structure learning for Gaifman models and learn relational features that can be used to derive feature representations from a knowledge base. These relational features are first-order rules that are then partially grounded and counted over local neighborhoods of a Gaifman model to obtain the feature representations. We propose a method for learning these relational features for a Gaifman model by using relational tree distances. Our empirical evaluation on real data sets demonstrates the superiority of our approach over classical rule-learning.