LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866929319868104704 |
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| author | Meyer, Lars-Peter Stadler, Claus Frey, Johannes Radtke, Norman Junghanns, Kurt Meissner, Roy Dziwis, Gordian Bulert, Kirill Martin, Michael |
| author_facet | Meyer, Lars-Peter Stadler, Claus Frey, Johannes Radtke, Norman Junghanns, Kurt Meissner, Roy Dziwis, Gordian Bulert, Kirill Martin, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_06917 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT Meyer, Lars-Peter Stadler, Claus Frey, Johannes Radtke, Norman Junghanns, Kurt Meissner, Roy Dziwis, Gordian Bulert, Kirill Martin, Michael Artificial Intelligence Computation and Language Databases 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. |
| title | LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT |
| topic | Artificial Intelligence Computation and Language Databases |
| url | https://arxiv.org/abs/2307.06917 |