LLM+KG@VLDB'24 Workshop Summary
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866912288289587200 |
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| author | Khan, Arijit Wu, Tianxing Chen, Xi |
| author_facet | Khan, Arijit Wu, Tianxing Chen, Xi |
| contents | The unification of large language models (LLMs) and knowledge graphs (KGs) has emerged as a hot topic. At the LLM+KG'24 workshop, held in conjunction with VLDB 2024 in Guangzhou, China, one of the key themes explored was important data management challenges and opportunities due to the effective interaction between LLMs and KGs. This report outlines the major directions and approaches presented by various speakers during the LLM+KG'24 workshop. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01978 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLM+KG@VLDB'24 Workshop Summary Khan, Arijit Wu, Tianxing Chen, Xi Databases Artificial Intelligence Machine Learning The unification of large language models (LLMs) and knowledge graphs (KGs) has emerged as a hot topic. At the LLM+KG'24 workshop, held in conjunction with VLDB 2024 in Guangzhou, China, one of the key themes explored was important data management challenges and opportunities due to the effective interaction between LLMs and KGs. This report outlines the major directions and approaches presented by various speakers during the LLM+KG'24 workshop. |
| title | LLM+KG@VLDB'24 Workshop Summary |
| topic | Databases Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2410.01978 |