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.
@article{arxiv.2308.06374,
title = {Large Language Models and Knowledge Graphs: Opportunities and Challenges},
author = {Jeff Z. Pan and Simon Razniewski and Jan-Christoph Kalo and Sneha Singhania and Jiaoyan Chen and Stefan Dietze and Hajira Jabeen and Janna Omeliyanenko and Wen Zhang and Matteo Lissandrini and Russa Biswas and Gerard de Melo and Angela Bonifati and Edlira Vakaj and Mauro Dragoni and Damien Graux},
journal= {arXiv preprint arXiv:2308.06374},
year = {2023}
}