Knowledge proliferation: a domain-driven framework for multi-faceted knowledge graph generation from business data

From Flat Storage to Structured Graphs: Adaptive Memory for Domain-Specific LLM Reasoning

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...

NORKE: A Framework for Normative Rule-Centric Knowledge Graph Construction using Language Models

Towards Building a Multi-Source Heterogeneous Knowledge Graph for Complex Material Question Answering

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.

Enabling Knowledge Graph Understanding at Scale with the EXplore Your Graphs ENgine (EXYGEN)

We present EXYGEN (EXplore Your Graphs ENgine), a framework for knowledge graph (KG) understanding that enables conversational access to KGs at scale. We address two questions in sequence. First, how effectively can LLMs perform text-to-SPARQL generation given only automatically derived structured metadata and small gr...

AI-Driven Knowledge Externalisation: From Unstructured Documents to Structured Data Models

The findings suggest that AI-based structured extraction may redefine how organisations formalise expertise, shifting from document-centric storage toward schema-driven knowledge architectures.

From Extraction to Governed Memory: Multi-Agent Knowledge Graph Construction with Domain-Expert Review

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.