Advancing Geospatial Data Integration: The Role of Prompt Engineering in Semantic Association with chatGPT
Year: 2024
Type: conference-paper
Abstract: 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.
Authors: Souza, Fabíola Andrade, Camboim, Silvana
Institutions: Universidade Federal da Bahia, Universidade Federal do Paraná
Language: English
Subfield: Geography, Planning and Development
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6893328011
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