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

• URL: http://arxiv.org/abs/2308.04814v1
• Date: Wed, 9 Aug 2023 09:12:35 GMT
• Title: Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges
• Authors: Gunjan Singh, Sumit Bhatia, Raghava Mutharaju
• Abstract summary: Onttologies are used in various domains, with RDF and OWL being prominent standards for domain development. As reasoning grows larger and more expressive, and traditional reasoners struggle to perform efficiently. Researchers have explored neuro-symbolic approaches that combine networks' learning capabilities with systems' reasoning abilities.
• Score: 6.295207672539996
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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