Abstract
This study presents our experiments with ChatGPT aimed at constructing a prototype domain ontology for World Heritage. We relied on the official United Nations Educational, Scientific and Cultural Organization classification published within the World Heritage Convention and its 2023 operational guidelines document as our trusted data sources. Our focus encompassed four main tasks: (i) gathering competency questions; (ii) constructing classes along with their taxonomy and definitions; (iii) populating the ontology with automatically generated instances; and (iv) evaluating the ontology through SPARQL queries generated from competency questions. By employing a naive approach paired with in-context learning and some prompt engineering techniques, our goal is to assess the capabilities and limitations of ChatGPT in supporting the construction of prototype ontologies by nonexperts or those with limited domain knowledge for application demonstration proposals. The results indicate that while this exciting area can expedite the ontology learning process, it also encounters certain technical limitations in uncovering implicit and less explicit semantics in input data. Additionally, proficient prompting skills are required to guide the model for producing accurate results.
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