LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT

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.

to appear in conference proceedings of AI-Tomorrow-23, 29.+30.6.2023 in Leipzig, Germany

References in corpus (5)

• Sparks of Artificial General Intelligence: Early experiments with GPT-4
• "I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data
• ChatGPT versus Traditional Question Answering for Knowledge Graphs: Current Status and Future Directions Towards Knowledge Graph Chatbots
• Context-based Ontology Modelling for Database: Enabling ChatGPT for Semantic Database Management
• Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG

Cited by in corpus (3)

• On the Use of Large Language Models to Generate Capability Ontologies
• Guiding Generative Storytelling with Knowledge Graphs
• LLM-KG-Bench 3.0: A Compass for SemanticTechnology Capabilities in the Ocean of LLMs