Advancing Geospatial Data Integration: The Role of Prompt Engineering in Semantic Association with chatGPT

Semantic interoperability is essential for integrating open geospatial collaborative and official data. While geosemantics has long been a topic of discussion, recent research has explored automated semantic integration without fully leveraging the capabilities of large language models (LLMs) in artificial intelligence. This study investigates using chatGPT-4 to semantically associate OpenStreetMap (OSM) tags with the Brazilian topographic mapping model, the Technical Specification for Structuring Vector Geospatial Data (ET- EDGV). Focusing on five classes within the buildings category, the study tested three data structuring methods: spreadsheets, OWL ontology,  and  XML.  Results  indicated  that  ontology  and  XML  formats  produced  more  accurate  semantic  associations  than spreadsheets, with OWL yielding the most coherent results. These findings underscore the importance of properly structured data to capture hierarchical relationships between concepts better. The study also noted the need for precise and detailed queries, highlighting some limitations in chatGPT's ability to understand complex geospatial model inputs. Further research is recommended to enhance LLMs' potential in facilitating semantic interoperability and to explore the role of prompt engineering in optimizing these interactions.