Enhancing Knowledge Graph Construction Using Large Language Models
Date Issued
2023-07
Author(s)
Trajanoska, Milena
Abstract
The growing trend of Large Language Models
(LLM) development has attracted significant attention, with mod els for various applications emerging consistently. However, the
combined application of Large Language Models with semantic
technologies for reasoning and inference is still a challenging task.
This paper analyzes how the current advances in foundational
LLM, like ChatGPT, can be compared with the specialized
pretrained models, like REBEL, for joint entity and relation
extraction. To evaluate this approach, we conducted several
experiments using sustainability-related text as our use case. We
created pipelines for the automatic creation of Knowledge Graphs
from raw texts, and our findings indicate that using advanced
LLM models can improve the accuracy of the process of creating
these graphs from unstructured text. Furthermore, we explored
the potential of automatic ontology creation using foundation
LLM models, which resulted in even more relevant and accurate
knowledge graphs.
(LLM) development has attracted significant attention, with mod els for various applications emerging consistently. However, the
combined application of Large Language Models with semantic
technologies for reasoning and inference is still a challenging task.
This paper analyzes how the current advances in foundational
LLM, like ChatGPT, can be compared with the specialized
pretrained models, like REBEL, for joint entity and relation
extraction. To evaluate this approach, we conducted several
experiments using sustainability-related text as our use case. We
created pipelines for the automatic creation of Knowledge Graphs
from raw texts, and our findings indicate that using advanced
LLM models can improve the accuracy of the process of creating
these graphs from unstructured text. Furthermore, we explored
the potential of automatic ontology creation using foundation
LLM models, which resulted in even more relevant and accurate
knowledge graphs.
Subjects
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