## 2 Literature Review  
The literature review is focused on the areas indicated in the Introduction section, namely natural language processing technologies, Large Language Models, the use of generative artificial intelligence and tools, and geographical information systems. The review used bibliographic and full-text databases such as Scopus, IEEE Xplore, Web of Science, SpringerLink, Science Direct, etc. Data mining involves the process of extracting valuable patterns and information from large datasets [6]. It is a knowledge-discovery process that aims to find trends and regularities within corporate data warehouses [7]. On the other hand, text mining is a sub-speciality of knowledge discovery from data (KDD) and is focused on extracting useful information from massive amounts of unstructured or semi-structured text data [8]. Text mining differs from data mining in that it processes natural unstructured text, which is intended for human consumption, rather than structured databases designed for programmatic processing [9]. Natural language processing (NLP) is a critical component of text mining, enabling the analysis of unstructured text data. NLP has evolved over time, and its sub-problems are well-documented in the field [10]. Researchers have refined NLP tools for real-world applications, including speech-to-speech translation engines, sentiment analysis, and mining social media for information [11]. Furthermore, the use of pre-trained models for NLP has been the subject of recent surveys, highlighting the advancements in this area [12]. In the context of healthcare, text mining has been applied to electronic health records to extract symptoms and patient information [13]. This application has revealed challenges, such as the need for improved reporting of patient demographic characteristics [13]. Additionally, text mining has been used in the analysis of biomedical literature, where the volume of published research has been increasing rapidly, necessitating advanced text mining techniques for knowledge extraction [14]. The process of converting text to ontology involves mapping free text to concepts in an ontology, which has been explored in various domains, such as biomedicine, computational biology, and computer science [15; 16; 17; 18]. This mapping is achieved through entity linking or annotation, which involves associating text with concepts in a knowledge graph or ontology [19; 20]. The process typically includes identifying appropriate text spans in narratives and then mapping these text spans to target concepts in an ontology [20]. Named-entity normalization is also utilized to build a mapping relationship between named entities in text and ontology [21]. Furthermore, the use of semantic annotators has been highlighted in expanding users' queries with concepts and terms from vocabularies/ontologies, as well as in classifying retrieved documents based on their content or specific topics [17]. Additionally, the process involves the generation of relevant ontology terms, their definitions, and the relationships between them [22]. The generation of domain-specific ontologies from unstructured text corpus has been emphasized, demonstrating the need for domain-independent ontology-generation methods [23]. Furthermore, the use of standard representations, such as ontologies, has been highlighted to provide a unified view of information extracted from data [24]. The process also involves linking text to semantically similar classes in an ontology, which has been achieved through exemplar-based algorithms [25]. Moreover, the detection of appropriate frames from input text has been shown to improve the design quality of resulting ontologies, as frames can be directly mapped to ontology design patterns [26]. To extract data for filling an ontology, various techniques and systems have been developed. These include Text-to-Onto, which utilizes statistical methods like generalized association rule discovery and symbolic methods such as lexico-syntactic pattern method [27].