2026-07-19T04:44:09Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:19476 2025-10-02T11:18:01Z ddc:004 open_access

Large Language Models and Knowledge Graphs: Opportunities and Challenges Pan, Jeff Z. Razniewski, Simon Kalo, Jan-Christoph Singhania, Sneha Chen, Jiaoyan Dietze, Stefan Jabeen, Hajira Omeliyanenko, Janna Zhang, Wen Lissandrini, Matteo Biswas, Russa de Melo, Gerard Bonifati, Angela Vakaj, Edlira Dragoni, Mauro Graux, Damien Large Language Models Pre-trained Language Models Knowledge Graphs Ontology Retrieval Augmented Language Models 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. Schloss-Dagstuhl - Leibniz Zentrum für Informatik 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 2023 Is Part Of TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1 Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/TGDK.1.1.2 urn:nbn:de:0030-drops-194766 https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.2 eng https://creativecommons.org/licenses/by/4.0/legalcode