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UID:pretalx-foss4g-2024-academic-track-BFMPHD@talks.staging.osgeo.org
DTSTART;TZID=-03:20241204T143000
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DESCRIPTION:Semantic interoperability is essential for integrating open geo
 spatial collaborative and official data. While geosemantics has long been 
 a topic of discussion\, recent research has explored automated semantic in
 tegration without fully leveraging the capabilities of large language mode
 ls (LLMs) in artificial intelligence. This study investigates using chatGP
 T-4 to semantically associate OpenStreetMap (OSM) tags with the Brazilian 
 topographic mapping model\, the Technical Specification for Structuring Ve
 ctor Geospatial Data (ET-EDGV). Focusing on five classes within the buildi
 ngs category\, the study tested three data structuring methods: spreadshee
 ts\, OWL ontology\, and XML. Results indicated that ontology and XML forma
 ts produced more accurate semantic associations than spreadsheets\, with O
 WL yielding the most coherent results. These findings underscore the impor
 tance of properly structured data to capture hierarchical relationships be
 tween concepts better. The study also noted the need for precise and detai
 led queries\, highlighting some limitations in chatGPT's ability to unders
 tand complex geospatial model inputs. Further research is recommended to e
 nhance LLMs' potential in facilitating semantic interoperability and to ex
 plore the role of prompt engineering in optimizing these interactions.
DTSTAMP:20260513T160939Z
LOCATION:Room II
SUMMARY:Advancing Geospatial Data Integration: The Role of Prompt Engineeri
 ng in Semantic Association with chatGPT - Fabíola Andrade
URL:https://talks.staging.osgeo.org/foss4g-2024-academic-track/talk/BFMPHD/
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