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The rapid adoption of Large Language Models (LLMs) in data management and Web applications has created new opportunities for natural language access, reasoning, and decision support. However, concerns about reliability, hallucinations, bias, and lack of explainability limit their use in sensitive scenarios. Graph-based representations, such as knowledge graphs and property graphs, offer a promising approach to address these limitations by providing structured knowledge that can validate and explain LLM outputs. The Graph-Enhanced LLMs for Trustworthy Web Data Management (GLOW) workshop explores how graph and Semantic Web technologies can enhance the trustworthiness of LLM-driven data management systems. In particular, it investigates graph-based retrieval and reasoning, ontology-guided modeling, and constraints that support more transparent and accountable LLM-based Web applications. Now in its second edition, GLOW provides a dedicated space for research at the intersection of Semantic Web, Knowledge Graphs, and LLM-driven data-centric AI. The workshop builds on the success of its first edition, which attracted more than 20 submissions and resulted in a high-quality technical program.
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