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Analyzing Llama 3-based Approach for Axiom Translation from Ontologies.

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Large language models (LLMs) show potential for translating ontology axioms into natural language, aiding expert evaluation. While Llama 3 demonstrated some accuracy, further improvements are needed for complex axioms in ontology development.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Knowledge Representation

Background:

  • Ontology development relies on collaboration between engineers and domain experts.
  • Translating complex ontology axioms into natural language improves understandability for non-expert stakeholders.
  • Evaluating ontologies is crucial for ensuring their accuracy and utility.

Purpose of the Study:

  • To investigate the efficacy of large language models (LLMs) in translating ontology axioms into natural language.
  • To assess the potential of LLM-generated translations to facilitate ontology evaluation.
  • To identify the strengths and limitations of LLMs in this axiom translation task.

Main Methods:

  • Utilized Llama 3, a large language model, for axiom translation.
  • Translated 1,192 ontology axioms across 19 distinct types from five published ontologies.
  • Conducted a manual evaluation of the translated axioms for accuracy and representational quality.

Main Results:

  • 13.67% of translated axioms were fully accurate, while 22.48% were inaccurate.
  • A significant portion, 63.84%, of translations were partially accurate.
  • LLMs showed competence in generating hierarchical natural language equivalents but struggled with complex axioms.

Conclusions:

  • LLMs show promise for supporting knowledge engineering in ontologies through axiom translation.
  • Opportunities exist for enhancing LLM performance via few-shot training or integration into knowledge engineering workflows.
  • Further research is needed to refine LLM capabilities for accurate and comprehensive ontology axiom translation.