## 2 Literature Review
Smart-dog and OntoPrima are systems that extract data from technical data sheets and text to populate ontologies using NLP techniques and domain expert validation [28; 29]. The integration of NLP with geospatial analytics represents a significant advancement in extracting and interpreting geospatial information from unstructured data. With the evolution of artificial intelligence, increased availability of digital text data, and enhanced computational power, NLP methods have become increasingly sophisticated, enabling theidentification of events, places, entities, and spatiotemporal patterns within geographic phenomena [30]. Exploring the capabilities of LLMs in geospatial data comprehension reveals that, beyond size, sophistication in model design is crucial for effectively synthesizing geospatial knowledge from textual information. Through innovative experimental approaches, such as probing for geo-coordinates and assessing geospatial reasoning with multidimensional scaling, insights into LLMs' geospatial awareness and reasoning abilities are uncovered. These findings highlight the potential and limitations of LLMs in facilitating informed geospatial decision-making, underscoring the need for more advanced developments in the field [31]. ChatGPT can be used for classifying domain terms according to upper ontologies [32]. This section delves into the use of ChatGPT as a tool for extracting data to build an ontology, providing valuable insights into the practical implementation of language models in ontology development. Another interesting use of ChatGPT is the automatic generation of SPARQL queries for ontologies [33]. GeoQA (Geographic Question Answering) [34] represents a burgeoning research domain within Geographic Information Science (GIScience), focusing on responding to geographic inquiries using natural language. Despite its promise, seamlessly merging structured geospatial data with unstructured natural language queries poses considerable challenges. Recent strides in LLMs have paved the way for natural language processing in diverse applications. This study introduces GeoQAMap, a novel system that translates natural language questions into SPARQL queries, retrieves geospatial data from Wikidata, and generates interactive maps as visual answers. Notably, GeoQAMap demonstrates potential for integration with additional geospatial datasets like OpenStreetMap and CityGML, facilitating complex geographic question answering involving advanced spatial operations. In the context of GeoQA systems, a comprehensive framework is proposed that integrates natural language processing, machine learning, ontological reasoning, and geographic information systems (GIS). By leveraging GIS functionalities and spatial ontologies, the system demonstrates enhanced capabilities in accurately answering diverse geographic questions. Through empirical evaluation, it achieves an impressive overall accuracy of 90%, underscoring the importance of spatial reasoning and GIS in advancing GeoQA systems [35]. Enhanced YAGO2 geo integrates OpenStreetMap data with YAGO2 to facilitate spatial reasoning for place-related queries. By translating natural language into logical representations and generating executable GeoSPARQL queries dynamically, it significantly improves question answering based on linked knowledge bases, as demonstrated on the Geospatial Gold Standard dataset [36]. Currently known advancements in the field of geospatial data analysis have been significantly enriched by the integration of LLMs like ChatGPT; LLMs are explored for their potential to interact with spatial databases using natural language. There is great interest in this innovative approach, as it presents a framework that could potentially transform the way geospatial data are analyzed by simplifying the generation and interpretation of geospatial SQL queries. Such developments are seen as pivotal in enhancing the accessibility and efficiency of complex geospatial analytics, marking a noteworthy progression in the field [37].