Large Language Models and Knowledge Graphs: Opportunities and Challenges
Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania,, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang,, Matteo Lissandrini, Russa Biswas, Gerard de Melo, Angela Bonifati, Edlira, Vakaj, Mauro Dragoni, Damien Graux
TL;DR
This paper discusses the evolving relationship between Large Language Models and Knowledge Graphs, highlighting opportunities, challenges, and the shift towards hybrid knowledge representations.
Contribution
It provides a comprehensive overview of the debates, opportunities, and research directions in integrating LLMs with Knowledge Graphs for improved knowledge representation.
Findings
Highlights the shift from explicit to hybrid knowledge representation
Identifies key challenges in combining LLMs with Knowledge Graphs
Suggests future research directions in the field
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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Taxonomy
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