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.
Authors
- Yu Zhang (ORCID: https://orcid.org/0000-0003-0540-6758)
- Huzefa Rangwala (ORCID: https://orcid.org/0000-0003-0435-0035)
- Danai Koutra (ORCID: https://orcid.org/0000-0002-3206-8179)
- Soji Adeshina (ORCID: https://orcid.org/0000-0003-3945-3640)
- Vassilis N. Ioannidis (ORCID: https://orcid.org/0000-0002-8367-0733)
- Xiusi Chen (ORCID: https://orcid.org/0000-0002-9713-8000)
- Qi Zhu (ORCID: https://orcid.org/0000-0003-0129-8542)
- Zhen Han (ORCID: https://orcid.org/0009-0003-8845-0507)
- Costas Mavromatis
Institutions
- Amazon (United States) (US)
- University of Illinois Urbana-Champaign (US)
- University of Michigan (US)
- Carnegie Mellon University (US)
- Texas A&M University (US)
Publication Details
- Published
- 2025-08-03
- DOI
- https://doi.org/10.1145/3711896.3737845
- Primary Topic
- Natural Language Processing Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00