Large Language Models and Knowledge Graphs: Opportunities and Challenges

Large Language Models and Knowledge Graphs: Opportunities and Challenges

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...

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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik 2023-12-01
Series:Transactions on Graph Data and Knowledge
Subjects:
large language models
pre-trained language models
knowledge graphs
ontology
retrieval augmented language models
Online Access:https://drops.dagstuhl.de/storage/08tgdk/tgdk-vol001/tgdk-vol001-issue001/TGDK.1.1.2/TGDK.1.1.2.pdf
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Summary: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.
ISSN:2942-7517

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