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.
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.
TOPIC 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
AIM & SCOPE
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
ORGANIZING COMMITTEE
Diego Rincon-Yanez PhD, Trinity College Dublin & ADAPT Centre
Wilma Schmidt, Bosch AI & Freie Universität Berlin
Bassem Sellami PhD., TallTech University
Martino Pulici, Bosch Center for Artificial Intelligence & LMU Munich
STEERING COMITEE
Prof. Evgeny Kharlamov, Bosch Center for Artificial Intelligence
Prof. Michael Cochez, Vrije Universiteit Amsterdam
Prof. Adrian Paschke, Freie Universität Berlin
Prof. Declan O’Sullivan, Trinity College Dublin & ADAPT Centre