[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-327127-105":59,"doc-detail-327127-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","specific-domain-ontology-construction-using-large-language-models-abstract","Specific Domain Ontology Construction Using Large Language Models - Abstract","","Ontologies provide structured representations that humans and systems can interpret, yet manual ontology crafting remains labor-intensive and leaves many domains without reference ontologies. Large Language Models (LLMs) offer strong natural-language understanding and have been explored to support ontology development. This work experiments with LLMs acting as domain experts to generate conceptual hierarchies from an initial concept, then evaluates 20 automatically constructed ontologies for Brazil’s Blue Amazon using GPT-3.5 and GPT-4 with human experts.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/specific-domain-ontology-construction-using-large-language-models-abstract/327127/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/specific-domain-ontology-construction-using-large-language-models-abstract/327127.png","ImageObject",300,407,{"name":92,"@type":93},"Logic","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",17,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How does the proposed approach use LLMs to build an ontology?","Question",{"text":113,"@type":114},"It uses LLMs in the role of domain experts to construct conceptual hierarchies from a given initial concept, then turns these outputs into domain ontologies for evaluation.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What domain was used to test the technique?",{"text":118,"@type":114},"The experiments target the Brazilian maritime territory, known as the Blue Amazon, and produce twenty automatically constructed ontologies for it.",{"name":120,"@type":111,"acceptedAnswer":121},"How were the LLM-generated ontologies evaluated?",{"text":122,"@type":114},"Twenty ontologies constructed using GPT-3.5 and GPT-4 were evaluated by human experts with deep knowledge of the domain.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},327127,1790436303,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},1099513958762,"https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253","Specific Domain Ontology Construction Using Large Language Models  \nVivian Magri Alcadi Soares 1 ,2 , Renata Wassermann 1 ,2  \n1University of So Paulo (USP)  \n2 Center for Artificial Intelligence (C4AI)  \n{[vivian.soares@alumni.usp.br](vivian.soares@alumni.usp.br), [renata@ime.usp.br](renata@ime.usp.br})[}](renata@ime.usp.br})  \narXiv :2606 .20691v1 [ cs .CL] 14 Jun 2026  \nAbstract  \nOntologies are useful structures to organize and maintain information that can be understood both by humans and systems. However, since their manual crafting is a laborious task, many specific domains lack reference ontologies. The outstanding ability for understanding natural language demonstrated by the Large Language Models (LLMs) has motivated their application to aid on a variety of fields, including on ontology development. This work presents the experimentation with a technique that uses LLMs in the role of domain experts to build conceptual hierarchies for a given initial concept. Twenty ontologies automatically constructed for the domain of the Brazilian maritime territory (a.k.a the Blue Amazon) using GPT-3.5 and GPT-4 were then evaluated by human experts. The models were able to construct overall coherent conceptualizations of the domain, but none of the outputs was completely satisfactory as a representation of the context without refinement.  \n1 Introduction  \nDeveloping an ontology defines a common vocabulary for researchers who need to share information of a domain, and is akin to building machine-interpretable definitions for a set of concepts and relations among them (Noy and Mcguinness 2001) . The manual construction of ontologies, however, is a strenuous endeavor, that requires appropriate knowledge. Many (semi-)automatic ontology extraction methods have been proposed over the years, ranging from basic rule-based and statistical approaches to complicated Machine Learning (ML) and hybrid architectures. Nevertheless, the problem of obtaining a well-structured, relevant and coherent with the desirable application ontology without a fair amount of human labor persisted.  \nIn this scenario, approaches using Large Language Models (LLMs) to develop ontologies have shown promising results. The methods, that seek to leverage the linguistic abilities these models gain on their extensive pre-training, appear as promising to bridge the gap of ontologies for specific domains that haven’t yet been provided a gold standard. Such is the case of the Brazilian maritime territory, a vast area with approximately the same size as the Amazon rainforest, and also referred to as the ”Blue Amazon”. It is not only a region of great economic and commercial value for Brazil, but the importance of its resources and multiple ecosystems  \nis invaluable, also playing a key role in climate regulation. Yet, the Blue Amazon is not well-known to the wider public. Information about it is dispersed in academic volumes and government reports, or in obscure databases (Pirozelli et al. 2022) .  \nThis research proposes an experiment with a LLMenabled algorithm to aid on the organization of information about the specific domain of the Blue Amazon. Since the application of methodologies of this kind to ontology learning is still recent and the evaluation process is not standardized, it was considered essential to also construct and conduct a thorough assessment of the outputs, involving humans with deep knowledge of the domain. The analysis of the results reveals the usefulness that can be perceived on the employment of generative AI to accelerate the construction of ontologies. Nonetheless, it also indicates that the full automation of this process is not yet recommended.  \n2 Literature Review  \nHandcrafting big ontologies is a difficult task. Over the past decades, various methods using linguistic, statistical, and logical techniques, usually associated with steps of the process known as the Ontology Learning Layer Cake, have contributed to the improvement of automatic ","cbCaimP7whBWaDmr","https://ap.wps.com/l/cbCaimP7whBWaDmr","pdf",372851,"English","# Abstract\n# 1 Introduction\n# 2 Literature Review","[{\"question\":\"How does the proposed approach use LLMs to build an ontology?\",\"answer\":\"It uses LLMs in the role of domain experts to construct conceptual hierarchies from a given initial concept, then turns these outputs into domain ontologies for evaluation.\"},{\"question\":\"What domain was used to test the technique?\",\"answer\":\"The experiments target the Brazilian maritime territory, known as the Blue Amazon, and produce twenty automatically constructed ontologies for it.\"},{\"question\":\"How were the LLM-generated ontologies evaluated?\",\"answer\":\"Twenty ontologies constructed using GPT-3.5 and GPT-4 were evaluated by human experts with deep knowledge of the domain.\"}]","Specific Domain Ontology Construction Using Large Language Models - Abstract | PDF",1789976489,25]