• Medientyp: Sonstige Veröffentlichung; E-Artikel
  • Titel: Large Language Models and Knowledge Graphs: Opportunities and Challenges
  • Beteiligte: Pan, Jeff Z. [Verfasser:in]; Razniewski, Simon [Verfasser:in]; Kalo, Jan-Christoph [Verfasser:in]; Singhania, Sneha [Verfasser:in]; Chen, Jiaoyan [Verfasser:in]; Dietze, Stefan [Verfasser:in]; Jabeen, Hajira [Verfasser:in]; Omeliyanenko, Janna [Verfasser:in]; Zhang, Wen [Verfasser:in]; Lissandrini, Matteo [Verfasser:in]; Biswas, Russa [Verfasser:in]; de Melo, Gerard [Verfasser:in]; Bonifati, Angela [Verfasser:in]; Vakaj, Edlira [Verfasser:in]; Dragoni, Mauro [Verfasser:in]; Graux, Damien [Verfasser:in]
  • Erschienen: Schloss-Dagstuhl - Leibniz Zentrum für Informatik, 2023
  • Sprache: Englisch
  • DOI: https://doi.org/10.4230/TGDK.1.1.2
  • Schlagwörter: Pre-trained Language Models ; Knowledge Graphs ; Retrieval Augmented Language Models ; Large Language Models ; Ontology
  • Entstehung:
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  • Beschreibung: 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.
  • Zugangsstatus: Freier Zugang