# Completion Reasoning Emulation for the Description Logic EL+

arXiv: https://arxiv.org/abs/1912.05063 · HTML: https://mlsanity.com/paper/1912.05063/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Neuro-Symbolic Artificial Intelligence: Current Trends](https://mlsanity.com/paper/2105.05330/) (2021)
- [Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing](https://mlsanity.com/paper/2210.15889/) (2022)
- [On the Capabilities of Pointer Networks for Deep Deductive Reasoning](https://mlsanity.com/paper/2106.09225/) (2021)
- [To Neuro-Symbolic Classification and Beyond by Compiling Description Logic Ontologies to Probabilistic Circuits](https://mlsanity.com/paper/2601.14894/) (2026)
- [Differentiable Fuzzy ALC: A Neural-Symbolic Representation Language for Symbol Grounding](https://mlsanity.com/paper/2211.12006/) (2022)
- [Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges](https://mlsanity.com/paper/2308.04814/) (2023)

## Abstract (arXiv)

We present a new approach to integrating deep learning with knowledge-based systems that we believe shows promise. Our approach seeks to emulate reasoning structure, which can be inspected part-way through, rather than simply learning reasoner answers, which is typical in many of the black-box systems currently in use. We demonstrate that this idea is feasible by training a long short-term memory (LSTM) artificial neural network to learn EL+ reasoning patterns with two different data sets. We also show that this trained system is resistant to noise by corrupting a percentage of the test data and comparing the reasoner's and LSTM's predictions on corrupt data with correct answers.