Nanopublication

ID

https://w3id.org/np/RA9XCq3hLViTumWKeAkj1FheofZqcg9aC09BIyFyZpvJg
Formats

Content

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    dct:description "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. This year the workshop welcomes a special track on RAG-systems for Human-AI Value Alignment, seeking contribution on RAG-based approaches for content moderation and harm reduction and to deal with human subjectivity. The workshop will be a full-day event featuring keynotes, paper presentations, and lightning talks on emerging RAG trends (e.g., agentic RAG), along with a dedicated session on the evaluation of RAG technologies aimed at building a community of practice on this theme.";
    schema:endDate "2026-10-26T18:00:00+01:00"^^xsd:dateTime;
    schema:location "Sezione 1";
    schema:startDate "2026-10-26T09:00:00+01:00"^^xsd:dateTime;
    schema:url <https://2026.rage-kg.org/>;
    rdfs:label "3rd International Workshop on Retrieval-Augmented Generation Enabled by Knowledge Graphs (RAGE-KG 2026)";
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  <https://orcid.org/0000-0002-1267-0234> foaf:name "Tobias Kuhn" .
  
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