# Differentiable Learning of Logical Rules for Knowledge Base Reasoning

arXiv: https://arxiv.org/abs/1702.08367 · HTML: https://mlsanity.com/paper/1702.08367/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2017.

## Built on by (most cited first, 8 of 8)

- [A Survey on Neural-symbolic Learning Systems](https://mlsanity.com/paper/2111.08164/) (2021)
- [Latent Logic Tree Extraction for Event Sequence Explanation from LLMs](https://mlsanity.com/paper/2406.01124/) (2024)
- [Federated Neuro-Symbolic Learning](https://mlsanity.com/paper/2308.15324/) (2023)
- [When does Chain-of-Thought Help: A Markovian Perspective](https://mlsanity.com/paper/2603.00306/) (2026)
- [Neural-Symbolic Logic Query Answering in Non-Euclidean Space](https://mlsanity.com/paper/2603.15633/) (2026)
- [An Investigation into Mini-Batch Rule Learning](https://mlsanity.com/paper/2106.10202/) (2021)
- [Adversarial Robustness of Representation Learning for Knowledge Graphs](https://mlsanity.com/paper/2210.00122/) (2022)
- [Uncertainty in Representation Learning on Knowledge Graphs](https://mlsanity.com/paper/2610.06974/) (2026)

## Abstract (arXiv)

We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning. This learning problem is difficult because it requires learning the parameters in a continuous space as well as the structure in a discrete space. We propose a framework, Neural Logic Programming, that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model. This approach is inspired by a recently-developed differentiable logic called TensorLog, where inference tasks can be compiled into sequences of differentiable operations. We design a neural controller system that learns to compose these operations. Empirically, our method outperforms prior work on multiple knowledge base benchmark datasets, including Freebase and WikiMovies.