Abstract
The rapid digitalisation of sectors like finance, healthcare, and education has resulted in fragmented data ecosystems where standards differ, infrastructures are isolated, and interoperability is limited. This paper examines how AI-powered Knowledge Graphs (KGS) can facilitate seamless integration and intelligent reasoning across distributed systems. We review advancements in knowledge graph development, multimodal data integration, and reasoning architectures, emphasising their role in closing interoperability gaps across domains. Key enabling technologies include graph neural networks (GNNs), large language models (LLMs) for ontology alignment, federated learning for privacy-preserving data sharing, and blockchain for provenance verification. Through case studies in financial compliance (Basel III, PSD2), healthcare data exchange (FHIR, HL7), and educational credentials (W3C Verifiable Credentials), we show how AI-enhanced KGs support regulation compliance, build trust, and facilitate decisions. Our findings indicate that AI-driven knowledge graphs will form the backbone of future interoperable digital infrastructure, fostering innovation in cross-industry collaboration and human-centric digital ecosystems.
Recommended Citation
Tripathi, Vishnu; Nimmagadda, Shastri; Mani, Neel; Mandal, Rishabh; and Helfert, Markus, "AI-Driven Knowledge Graphs for Interoperable Digital
Ecosystems" (2025). ACIS 2025 Proceedings. 128.
https://aisel.aisnet.org/acis2025/128