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

TL;DR

This paper reviews the current state and challenges of neuro-symbolic reasoning approaches for RDF and Description Logic ontologies, highlighting their potential to address scalability and noise issues in large, complex knowledge bases.

Contribution

It provides a comprehensive overview of neuro-symbolic reasoning methods for RDF and Description Logics, discussing techniques, tasks, and existing research efforts in this emerging field.

Findings

Neuro-symbolic approaches aim to improve reasoning over large ontologies.

Current methods face scalability and noise challenges.

The survey highlights promising directions for future research.

Abstract

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.