SKnow-LLM Workshop: Structured Knowledge for Large Language Models

Frontier large language models (LLMs) have demonstrated remarkable performance across various knowledge-intensive enterprise tasks. However, these models are primarily trained on unstructured, general knowledge, which limits their effectiveness in domain-specific applications-particularly when tasks involve structured data sources or sensitive enterprise information. We propose the first Structured Knowledge for Large Language Models Workshop - SKnow-LLM, which aims to bridge this gap by promoting research on innovative methodologies and practical applications in this area. Through keynote talks, panel discussions and paper presentations, the workshop will foster in-depth discussions on recent advances, identify existing challenges, and explore promising directions for integrating structured knowledge into LLMs.

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Published
2025-08-03
DOI
https://doi.org/10.1145/3711896.3737845
Primary Topic
Natural Language Processing Techniques
Type
article
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article

SKnow-LLM Workshop: Structured Knowledge for Large Language Models

Yu Zhang, Huzefa Rangwala, Danai Koutra, Soji Adeshina et al.
Natural Language Processing Techniques
article

SKnow-LLM Workshop: Structured Knowledge for Large Language Models

Yu Zhang, Huzefa Rangwala, Danai Koutra, Soji Adeshina, Vassilis N. Ioannidis, Xiusi Chen, Qi Zhu, Zhen Han, Costas Mavromatis
article en

Abstract

Frontier large language models (LLMs) have demonstrated remarkable performance across various knowledge-intensive enterprise tasks. However, these models are primarily trained on unstructured, general knowledge, which limits their effectiveness in domain-specific applications-particularly when tasks involve structured data sources or sensitive enterprise information. We propose the first Structured Knowledge for Large Language Models Workshop - SKnow-LLM, which aims to bridge this gap by promoting research on innovative methodologies and practical applications in this area. Through keynote talks, panel discussions and paper presentations, the workshop will foster in-depth discussions on recent advances, identify existing challenges, and explore promising directions for integrating structured knowledge into LLMs.

Amazon (United States) (US), University of Illinois Urbana-Champaign (US), University of Michigan (US), Carnegie Mellon University (US), Texas A&M University (US)
Quality Education
Openalex Percentile: Top 90%
Natural Language Processing Techniques
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SKnow-LLM Workshop: Structured Knowledge for Large Language Models — Yu Zhang, Huzefa Rangwala, et al. · (2025) | TGRS Research Map | TGRS