Neuro-Symbolic AI in Database Systems: A Systematic Review of Query Processing, Knowledge Graph Reasoning, Data Storage and Integration
Uchenna Jeremiah Nzenwata, Folasade Yetunde Ayankoya, Shade Oluwakemi Kuyoro, Emokiniovo Edwin
Asian Journal of Research in Computer Science · pp. 1–19 · Published 25 Nov 2025
10.9734/ajrcos/2025/v18i12787Abstract
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.
Cited by 1
Yu Guo, Ruoying Jia · Proceedings of the 2025 6th International Conference on Computer Science and Management Technology · 2025
Related research
- Challenges in the Process of Development and Maintenance of a Modern Bulgarian Food Composition Database: FAO’s Project – Real Achievements — shares topic coverage
- Application of Database Technology in the Software of Seismic Data Acquisition — shares topic coverage
- Creation of a GIS Database for the Assessment of Secondary Schools within Afikpo — shares topic coverage
- Food Image Calorie Estimation Using Deep Learning Based Pre-Trained Image Detection Architecture and Machine Learning Algorithms — shares topic coverage
- The Bradford Multi-Modal Gait Database: Gateway to Using Static Measurements to Create a Dynamic Gait Signature — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
1
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.