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  Large Language Models and Knowledge Graphs: Opportunities and Challenges

Pan, J. Z., Razniewski, S., Kalo, J.-C., Singhania, S., Chen, J., Dietze, S., et al. (2023). Large Language Models and Knowledge Graphs: Opportunities and Challenges. Retrieved from https://arxiv.org/abs/2308.06374.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-A223-4 Version Permalink: https://hdl.handle.net/21.11116/0000-000D-A224-3
Genre: Paper

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arXiv:2308.06374.pdf (Preprint), 749KB
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 Creators:
Pan, Jeff Z.1, Author
Razniewski, Simon1, Author           
Kalo, Jan-Christoph1, Author
Singhania, Sneha2, Author           
Chen, Jiaoyan1, Author
Dietze, Stefan1, Author
Jabeen, Hajira1, Author
Omeliyanenko, Janna1, Author
Zhang, Wen1, Author
Lissandrini, Matteo1, Author
Biswas, Russa1, Author
de Melo, Gerard1, Author
Bonifati, Angela1, Author
Vakaj, Edlira1, Author
Dragoni, Mauro1, Author
Graux, Damien1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

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Free keywords: Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL
 Abstract: Large Language Models (LLMs) have taken Knowledge Representation -- and the
world -- by storm. This inflection point marks a shift from explicit knowledge
representation to a renewed focus on the hybrid representation of both explicit
knowledge and parametric knowledge. In this position paper, we will discuss
some of the common debate points within the community on LLMs (parametric
knowledge) and Knowledge Graphs (explicit knowledge) and speculate on
opportunities and visions that the renewed focus brings, as well as related
research topics and challenges.

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Language(s): eng - English
 Dates: 2023-08-112023
 Publication Status: Published online
 Pages: 30 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 2308.06374
BibTex Citekey: Pan2308.06374
URI: https://arxiv.org/abs/2308.06374
 Degree: -

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