Background. Endowing intelligent systems with semantic data commonly requires
designing and instantiating ontologies with domain-specific knowledge.
Especially in the early phases, those activities are typically performed
manually by human experts possibly leveraging on their own experience. The
resulting process is therefore time-consuming, error-prone, and often biased by
the personal background of the ontology designer. Objective. To mitigate that
issue, we propose a novel domain-independent approach to automatically
instantiate ontologies with domain-specific knowledge, by leveraging on large
language models (LLMs) as oracles. Method. Starting from (i) an initial schema
composed by inter-related classes andproperties and (ii) a set of query
templates, our method queries the LLM multi- ple times, and generates instances
for both classes and properties from its replies. Thus, the ontology is
automatically filled with domain-specific knowledge, compliant to the initial
schema. As a result, the ontology is quickly and automatically enriched with
manifold instances, which experts may consider to keep, adjust, discard, or
complement according to their own needs and expertise. Contribution. We
formalise our method in general way and instantiate it over various LLMs, as
well as on a concrete case study. We report experiments rooted in the
nutritional domain where an ontology of food meals and their ingredients is
semi-automatically instantiated from scratch, starting from a categorisation of
meals and their relationships. There, we analyse the quality of the generated
ontologies and compare ontologies attained by exploiting different LLMs.
Finally, we provide a SWOT analysis of the proposed method.

使用大型语言模型自动填充本体论与领域特定知识，通过查询模板生成实例，从而快速丰富本体论。