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Computer Science > Artificial Intelligence

arXiv:2307.06917 (cs)
[Submitted on 13 Jul 2023]

Title:LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT

Authors:Lars-Peter Meyer, Claus Stadler, Johannes Frey, Norman Radtke, Kurt Junghanns, Roy Meissner, Gordian Dziwis, Kirill Bulert, Michael Martin
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Abstract:Knowledge Graphs (KG) provide us with a structured, flexible, transparent, cross-system, and collaborative way of organizing our knowledge and data across various domains in society and industrial as well as scientific disciplines. KGs surpass any other form of representation in terms of effectiveness. However, Knowledge Graph Engineering (KGE) requires in-depth experiences of graph structures, web technologies, existing models and vocabularies, rule sets, logic, as well as best practices. It also demands a significant amount of work. Considering the advancements in large language models (LLMs) and their interfaces and applications in recent years, we have conducted comprehensive experiments with ChatGPT to explore its potential in supporting KGE. In this paper, we present a selection of these experiments and their results to demonstrate how ChatGPT can assist us in the development and management of KGs.
Comments: to appear in conference proceedings of AI-Tomorrow-23, 29.+30.6.2023 in Leipzig, Germany
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Databases (cs.DB)
Cite as: arXiv:2307.06917 [cs.AI]
  (or arXiv:2307.06917v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2307.06917
Journal reference: Informatik aktuell. First Working Conference on Artificial Intelligence Development for a Resilient and Sustainable Tomorrow 2023. AIDRST 2023. p. 103-115
Related DOI: https://doi.org/10.1007/978-3-658-43705-3_8

Submission history

From: Lars-Peter Meyer [view email]
[v1] Thu, 13 Jul 2023 17:31:41 UTC (14 KB)
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