# Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges

arXiv: https://arxiv.org/abs/2308.04814 · HTML: https://mlsanity.com/paper/2308.04814/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 91 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 22 of 22)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [OWL2Vec*: Embedding of OWL Ontologies](https://mlsanity.com/paper/2009.14654/) (2020)
- [Neuro-Symbolic Artificial Intelligence: Current Trends](https://mlsanity.com/paper/2105.05330/) (2021)
- [EL Embeddings: Geometric construction of models for the Description Logic EL ++](https://mlsanity.com/paper/1902.10499/) (2019)
- [Ontology Reasoning with Deep Neural Networks](https://mlsanity.com/paper/1808.07980/) (2018)
- [OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction](https://mlsanity.com/paper/1804.10922/) (2018)
- [Faithiful Embeddings for EL++ Knowledge Bases](https://mlsanity.com/paper/2201.09919/) (2022)
- [Knowledge Graphs on the Web -- an Overview](https://mlsanity.com/paper/2003.00719/) (2020)
- [Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations](https://mlsanity.com/paper/1802.00864/) (2018)
- [Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective](https://mlsanity.com/paper/2202.07412/) (2022)
- [Completion Reasoning Emulation for the Description Logic EL+](https://mlsanity.com/paper/1912.05063/) (2019)
- [The CaLiGraph Ontology as a Challenge for OWL Reasoners](https://mlsanity.com/paper/2110.05028/) (2021)
- [Reasoning over RDF Knowledge Bases using Deep Learning](https://mlsanity.com/paper/1811.04132/) (2018)
- [On the Capabilities of Pointer Networks for Deep Deductive Reasoning](https://mlsanity.com/paper/2106.09225/) (2021)
- [Deep Learning for Ontology Reasoning](https://mlsanity.com/paper/1705.10342/) (2017)
- [Optimizing Heuristics for Tableau-based OWL Reasoners](https://mlsanity.com/paper/1810.06617/) (2018)
- [Learning the Right Expansion-ordering Heuristics for Satisfiability Testing in OWL Reasoners](https://mlsanity.com/paper/1904.09443/) (2019)
- [Explainable Deep RDFS Reasoner](https://mlsanity.com/paper/2002.03514/) (2020)
- [Why Settle for Just One? Extending EL++ Ontology Embeddings with Many-to-Many Relationships](https://mlsanity.com/paper/2110.10555/) (2021)

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

- [Bridging the Gap: Representation Spaces in Neuro-Symbolic AI](https://mlsanity.com/paper/2411.04393/) (2024)

## Abstract (arXiv)

Ontologies are used in various domains, with RDF and OWL being prominent standards for ontology development. RDF is favored for its simplicity and flexibility, while OWL enables detailed domain knowledge representation. However, as ontologies grow larger and more expressive, reasoning complexity increases, and traditional reasoners struggle to perform efficiently. Despite optimization efforts, scalability remains an issue. Additionally, advancements in automated knowledge base construction have created large and expressive ontologies that are often noisy and inconsistent, posing further challenges for conventional reasoners. To address these challenges, researchers have explored neuro-symbolic approaches that combine neural networks' learning capabilities with symbolic systems' reasoning abilities. In this chapter,we provide an overview of the existing literature in the field of neuro-symbolic deductive reasoning supported by RDF(S), the description logics EL and ALC, and OWL 2 RL, discussing the techniques employed, the tasks they address, and other relevant efforts in this area.