Large Language Models and Knowledge Graphs: Opportunities and Challenges

• DOI: 10.48550/arxiv.2308.06374
• Publication Date: 2023-01-01
• Authors: 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
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; Ontology; Computer Science - Artificial Intelligence; Retrieval Augmented Language Models; Pre-trained Language Models; QA75.5-76.95; 004; data management, machine learning, knowledge graph; pre-trained language models; Large Language Models; knowledge graphs; Artificial Intelligence (cs.AI); Electronic computers. Computer science; [INFO]Computer Science [cs]; large language models; ontology; ddc:004; Knowledge Graphs; Computation and Language (cs.CL); retrieval augmented language models

30 pages<br/>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.<br/>