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 deadline

Fri, Jul 24, 2026

Submission deadline

Paper Submission

Research Paper Submission Tracks

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

Call for Papers Description

Short papers describing novel research contributions and preliminary results (4-6 pages). Full papers describing novel research contributions of extended length (8-12 pages).