TL;DR
Preliminary evidence is provided that domain-driven decomposition and identity-role modeling can support the construction of multi-faceted, natively federated knowledge graphs for enterprise knowledge management.
Large language models (LLMs) achieve strong general performance but struggle in specialized domains requiring structured, evolving knowledge. Existing context adaptation frameworks, such as Dynamic Cheatsheet and Agentic Context Engineering (ACE), accumulate domain knowledge as flat bullet-point lists, ignoring the rel...
This investigation proposes the NORKE framework, a structure-aware solution for constructing KGs from normative documents that leverages the inherent normative hierarchy, to extract and formalize rules as semantically cohesive RDF triples.
Results indicate that integrating multi-source domain knowledge with relation-preserved retrieval and attribute-supported filtering provides more focused and inspectable evidence, thereby supporting more accurate complex material question answering.
Results show that structured schema context and lightweight prompting can substantially reduce reliance on fine-tuning for scalable conversational access to KGs, though closing the remaining gap to fully fine-tuned approaches will likely require reducing dependence on curated question-query exemplars.
The findings suggest that AI-based structured extraction may redefine how organisations formalise expertise, shifting from document-centric storage toward schema-driven knowledge architectures.
MAGG is proposed, a principled multi-agent framework for constructing Governed Knowledge Graphs that introduces explicit governance decisions for reliable and trustworthy knowledge sharing and demonstrates its effectiveness.
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