Large language models as oracles for instantiating ontologies with domain-specific knowledge

• URL: http://arxiv.org/abs/2404.04108v1
• Date: Fri, 5 Apr 2024 14:04:07 GMT
• Title: Large language models as oracles for instantiating ontologies with domain-specific knowledge
• Authors: Giovanni Ciatto, Andrea Agiollo, Matteo Magnini, Andrea Omicini,
• Abstract summary: Endowing intelligent systems with semantic data commonly requires designing and instantiating with domain-specific knowledge. The resulting experience process is therefore time-consuming, error-prone, and often biased by the personal background of ontology designer. We propose a novel domain-independent approach to automatically instantiate with domain-specific knowledge.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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