Journal of Innovation Call for Papers – November 2026
The Role of Knowledge Graphs in the Age of AI
As artificial intelligence reshapes industries, institutions, and daily life, the need for
structured, explainable, and context-rich knowledge systems has never been greater. In this edition, Journal of Innovation seeks original contributions that examine the growing role of knowledge graphs in enabling more trustworthy, intelligent, and interoperable AI systems—grounded in lessons learned from early real-world deployments and implementation at scale.
Knowledge graphs are emerging as a critical foundation for the next generation of AI, helping connect fragmented and siloed data, support reasoning, improve explainability, and enable more adaptive and context-aware decision-making. From enterprise intelligence and scientific discovery to digital twins, automation, and responsible AI governance, their relevance is expanding rapidly across domains.
Topics of interest include, but are not limited to:
- Knowledge graphs as foundations for next-generation AI architectures
- Neuro-symbolic AI and graph-based reasoning systems
- Retrieval-augmented generation (RAG) with knowledge graphs
- Knowledge graph construction, curation, and maintenance at scale
- Ontologies, semantic interoperability, and data integration in complex enterprises
- Explainability, traceability, and trust in AI systems enabled by knowledge graphs
- Knowledge graphs for enterprise decision intelligence and automation
- Applications of knowledge graphs in digital twins, supply chains, healthcare, finance, and scientific research
- Real-time and dynamic knowledge graphs for adaptive systems
- Multi-modal knowledge representation across text, image, sensor, and structured data
- Governance, quality, and lifecycle management of enterprise knowledge graphs
- Privacy, security, and responsible use of graph-based AI systems
- Human-AI collaboration supported by semantic knowledge systems
- Benchmarks, metrics, and evaluation methods for graph-enhanced AI performance
Priority will be given to submissions that address strategic and practical challenges in deploying knowledge graphs in real-world AI environments, including integration with large language models, organizational adoption, governance, and measurable business or societal impact.
Join us in shaping a conversation central to the future of AI. Let us reimagine knowledge not merely as data to be processed, but as connected intelligence that can make AI systems more transparent, reliable, and effective.
