[DL] Call for Papers - SKGI 2025 – Scaling Knowledge Graphs for Industry: LLMs meet KGs (Half-day)

[Diego Rincon]

----  Apologies for the cross posting

*📣 Call for Papers - SKGI 2025 – Scaling Knowledge Graphs for Industry:
LLMs meet KGs (Half-day)*

*https://sites.google.com/view/skgi/call
<https://sites.google.com/view/skgi/call> *

*🎯 Workshop Overview*

The 2nd International Workshop on Scaling Knowledge Graphs for Industry
(SKGI) (half-day) will explore the convergence of Knowledge Graphs (KGs)
and Large Language Models (LLMs), with a focus on building scalable, robust
and trustworthy AI systems for real-world industrial settings. While
Knowledge Graphs have long been foundational in semantic technologies,
their integration with neural systems like LLMs presents new challenges and
opportunities—from data ingestion and dynamic graph construction to
retrieval-augmented generation (Graph-RAG), hybrid reasoning, and
human-centered interfaces.

   - 🗓️ day tbc, September 03 - 05, 2025 | 📍 Vienna, Austria
   | 📚 Co-located with SEMANTiCS 2025
   - 🔗 Submission portal: Open Review
   - 🌐 Workshop website: https://sites.google.com/view/skgi
   - 🔗 Previous edition: SKGI 2024 Website

This workshop aims to discuss how to bring symbolic-neural systems to
industrial scale, with special emphasis on scalability, trustworthiness,
and real-world deployment. This workshop is the second edition of the
International Workshop on Scaling Knowledge Graphs for Industry (SKGI). The
first edition was held in 2024 and focused on the practical challenges and
solutions for deploying Knowledge Graphs at industrial scale.

This second edition expands the scope to explicitly include Large Language
Models (LLMs) and their intersection with Knowledge Graphs. This reflects
the increasing demand in industry for hybrid symbolic-neural systems that
combine the robustness and structure of KGs with the flexibility and
generative power of foundation models.

*🔬 Topics of Interest*

We invite contributions from both academia and industry across a wide range
of topics including:

GraphRAGs Oriented Topics

   - Graph-based Retrieval-Augmented Generation (GraphRAG)
   - Using LLMs for semantic data ingestion and KG enrichment
   - Evaluation frameworks for hybrid LLM-KG systems
   - LLM hallucination mitigation using structured knowledge
   - Real-world industrial case studies of KG/LLM integration
   - Scalable reasoning and inference over symbolic-neural pipelines

Other LLM+KGs applications in the Real-World Settings

   - Scalable construction and maintenance of KGs
   - Streaming and temporal data in knowledge graphs
   - Energy-efficient and sustainable KG-based AI systems
   - Trustworthy AI: explainability and traceability through KGs
   - User interfaces and human-in-the-loop systems for KGs

*📄 Submission Guidelines*

   - Long papers (8–12 pages): Research, In-use systems, or comprehensive
   LLM-based systems evaluations
   - Short/position papers (4–6 pages): works in progress, early result
   exposure, position statements, emerging systems.
   - All submissions will undergo peer-review by at least two
   research-community active members. Accepted papers will be submitted to
    CEUR-WS proceedings.
   - All deadlines are set for 11:59 pm, Anywhere on Earth time (UTC-12)
   - Presentation of accepted work at the workshop is mandatory.
   - Page limits exclude references.
   - Papers must be submitted in PDF format using the CEUR-WS single-column
   template. Templates are available from
   https://www.overleaf.com/latex/templates/template-for-submissions-to-ceur-workshop-proceedings-ceur-ws-dot-org/wqyfdgftmcfw
   - Submission Platform: TBA Open Review - Link Soon

*📆 Important Dates*

   - Abstract Submission: May 31, 2025
   - Paper Submission Deadline: June 9, 2025
   - Author Notification: July 18, 2025
   - Camera-Ready Submission: August 18, 2025
   - Workshop Date: September 3-5, 2025 (TBA)

*👥 Who Should Participate?*

   - Researchers in Semantic Web, Knowledge Representation, LLMs, and
   Hybrid AI
   - Engineers and practitioners deploying KG/LLM systems in industry
   - AI researchers interested in building trustworthy and efficient AI
   pipelines
   - Students and early-career researchers working at the intersection of
   symbolic and neural AI

Kind Regards
*Diego Rincon-Yanez* | Research Fellow
ADAPT Centre
O’Reilly Institute p: +353 (0) 1 896 xxxx
Trinity College Dublin m: +353 (0) 86 xxx xxxx
Dublin 2 diego.rincon at adaptcentre.ie
Ireland www.adaptcentre.ie
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