Artificial Intelligence
RAGE-KG 2026: Retrieval-Augmented Generation Enabled by Knowledge Graphs
About us
RAGE-KG explores the state of the art and goes beyond in integrating Retrieval-Augmented Generation (RAG) with Knowledge Graphs as well as the synergies between Large Language Models and the Linked Open Data ecosystem. We aim to foster innovative RAG architectures relying on Semantic Web standards and new approaches to make Linked Open Data usable by LLMs, enhancing their ability to generate reliable, verifiable and context-aware responses based on structured, decentralized and authoritative data sources.
Important Dates
Fri, Jul 17, 2026
Abstract registration deadlineFri, Jul 24, 2026
Submission deadlineCall For Paper
RAG architectures leveraging Knowledge Graphs, Semantic Web standards and Linked Data
RAG design patterns including GraphRAG and AI Agents
Evaluating RAG architectures with structured data
Training and fine-tuning LLMs with structured data
Prompting Language Models with structured data
Language Model-supported and ontology-supported SPARQL query generation
Neurosymbolic approaches for integrating Language Models with Linked Open Data, Semantic Web and Knowledge Graphs
Use Cases, Work-In-Progress and, especially, Bold Proposals for RAG systems
RAG-based approaches for content moderation and harm reduction
Designing RAG systems to deal with human subjectivity
Short papers describing novel research contributions and preliminary results (4-6 pages). Full papers describing novel research contributions of extended length (8-12 pages).