Enhancing Knowledge Graph Construction Using Large Language Models
This work addresses the problem of efficient knowledge graph construction from text for researchers and practitioners in semantic technologies, but it is incremental as it builds on existing methods like REBEL.
The paper tackled the challenge of combining Large Language Models with semantic technologies for knowledge graph construction, finding that using advanced LLMs like ChatGPT improved accuracy in creating graphs from unstructured text and enabled automatic ontology creation for more relevant results.
The growing trend of Large Language Models (LLM) development has attracted significant attention, with models 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.