Neuro-Symbolic AI in Database Systems: A Systematic Review of Query Processing, Knowledge Graph Reasoning, Data Storage and Integration
Uchenna Jeremiah Nzenwata
Department of Computer Science, Babcock University, Ilishan Remo, Ogun State, Nigeria.
Folasade Yetunde Ayankoya
Department of Computer Science, Babcock University, Ilishan Remo, Ogun State, Nigeria.
Shade Oluwakemi Kuyoro
Department of Computer Science, Babcock University, Ilishan Remo, Ogun State, Nigeria.
Emokiniovo Edwin
*
Department of Computer Science, Babcock University, Ilishan Remo, Ogun State, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Neuro-symbolic AI is changing how database systems work by combining neural networks' ability to recognize patterns in data with the logical reasoning of symbolic AI. This review looks at how these two approaches work together to improve database systems in areas like query processing, knowledge graph reasoning, and data representation. We also examine how studies from 2020 to 2025 measure performance, report improvements, and discuss limitations. Several studies show that neuro-symbolic methods can boost accuracy by 10–20% and cut costs by up to 80% compared to older methods. These systems are also better at handling unstructured data, dealing with incomplete information, and working without fixed schemas. Still, scalability is a significant challenge, with most studies noting problems as systems grow larger. Integrating these technologies can also be complex and often requires advanced engineering. In summary, while neuro-symbolic AI has great promise for database technology, more research and development are needed before it can be widely used in large organizations.
Keywords: Neuro-symbolic, neural-symbolic, NSAI, database, query optimization, semantic search, information retrieval, knowledge graph, data integration