Large Language Model for Ontology Learning In Drinking Water Distribution Network Domain

Currently, most ontologies are created manually, which is time-consuming and labour-intensive. Meanwhile, the advanced capabilities of Large Language Models (LLMs) have proven beneficial in various domains, significantly improving the efficiency of text processing and text generation. Therefore, this paper focuses on the use of LLMs for ontology learning. It uses a manual ontology construction method as a basis to facilitate the LLMs for ontology learning. The proposed approach is based on Retrieval Augmented Generation (RAG), and passed queries to LLMs are based upon the manual ontology method – UPON Lite ontology. Two different variants of LLMs have been experimented with, and they all demonstrate the capability of ontology learning to varying degrees. This approach shows promising initial results in the direction of (semi-) automated ontology learning using LLMs and makes the ontology construction process easier for people without prior domain expertise.The final ontology was evaluated by the domain expert and ranked according to the defined criteria. Based on the evaluation results, the final ontology could be used as a base version, but it requires further fine-tuning by domain experts to ensure its accuracy and completeness.