Jul 2026· International Journal of Interactive Mobile Technologies (ijim)· Vol 20· 1 citation· 21 references
TL;DR
A context-aware, artificial intelligence (AI)-driven framework that enables ERP systems to interpret and adapt to evolving legal requirements is proposed that contributes to the development of intelligent and explainable ERP systems capable of sustaining real-time compliance in dynamic regulatory environments.
Abstract
Traditional enterprise resource planning (ERP) systems struggle to adapt to rapidly evolving legal environments because of their static architectures and dependence on manual updates, particularly in developing economies. This study addresses this limitation by proposing a context-aware, artificial intelligence (AI)-driven framework that enables ERP systems to interpret and adapt to evolving legal requirements. The framework integrates ontology-based reasoning, natural language processing (NLP), and adaptive learning to transform legislative changes into machine-interpretable rules and executable process updates, supported by human validation to ensure accuracy and accountability. Designed using a design-oriented research approach, the framework establishes a structured architecture that supports continuous, traceable, and adaptive compliance. The findings demonstrate the framework’s potential to enhance regulatory alignment, improve transparency, and reduce dependence on manual system updates. The study contributes to the development of intelligent and explainable ERP systems capable of sustaining real-time compliance in dynamic regulatory environments.
Overall, the findings suggest that MALTG provides a formally grounded and reproducible approach for automating multi-framework LegalTech governance conformance assessment while maintaining semantic and structural traceability between normative models and operational architectures.
Patricio M. Paccha-Angamarca, Erwin J. Sacoto-Cabrera, Víctor V. Velepucha-Bonett· Information· 0 citations
The results support the feasibility of ontology-driven generation for static-classification systems, whereas arithmetic risk computation and temporal event processing remain better suited to complementary procedural technologies.
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This paper presents a framework integrating Knowledge Graphs and Large Language Models to support a more extensible design review environment, and demonstrates its ability to retrieve and execute existing rules from the KG, capture new requests during design, and maintain a verifiable, adaptive compliance checking system.
Maen Alnuzha, Tanya Bloch· Journal of Information Techn...· 1 citation
This paper presents an analytics-based framework that delivers practical, context-specific guidance to change managers to compare process models with industrial standards, align process ontologies, and translate detected deviations into actionable recommendations.
Domonkos Gáspár, Ildikó Szabó, Katalin Ternai et al.· Journal of Industrial Integr...· 0 citations
The findings advocate for the integration of AI-powered pipelines within ERP systems as a transformative approach to enable scalable, intelligent, and high-fidelity data processing, essential for next- generation enterprise software resilience and performance.
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