arXiv:2606.20691v1 Announce Type: cross Abstract: Ontologies 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 concep
The rapid advancements and widespread adoption of Large Language Models (LLMs) make their application to complex tasks like ontology construction a natural and timely next step, addressing existing bottlenecks in knowledge organization.
Automating specific domain ontology construction using LLMs significantly reduces the manual effort and expertise required, accelerating knowledge representation and enabling more sophisticated AI applications across specialized fields.
The barrier to creating and maintaining detailed, machine-readable knowledge structures for niche domains is lowered, potentially democratizing access to structured information and improving AI system performance.
- · AI developers
- · Domain experts
- · Data science industry
- · Knowledge management platforms
- · Manual ontology developers
- · Companies reliant on unstructured data
LLMs can efficiently generate ontologies for specific domains, improving data organization.
Enhanced domain ontologies lead to more accurate and context-aware AI systems and agents.
The widespread availability of specialized ontologies could accelerate discovery and innovation in various scientific and industrial sectors.
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Read at arXiv cs.AI