Enriching Ontologies with Disjointness Axioms using Large Language Models

Ontologies often lack explicit disjointness declarations between classes, despite their usefulness for sophisticated reasoning and consistency checking in Knowledge Graphs. In this study, we explore the potential of Large Language Models (LLMs) to enrich ontologies by identifying and asserting class...

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Bibliographic Details
Published inarXiv.org
Main Authors Crum, Elias, De Santis, Antonio, Ovide, Manon, Pan, Jiaxin, Pisu, Alessia, Lazzari, Nicolas, Rudolph, Sebastian
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 04.10.2024
Subjects
Axioms
Design optimization
Enrichment
Knowledge representation
Large language models
Ontology
Prompt engineering
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Summary:Ontologies often lack explicit disjointness declarations between classes, despite their usefulness for sophisticated reasoning and consistency checking in Knowledge Graphs. In this study, we explore the potential of Large Language Models (LLMs) to enrich ontologies by identifying and asserting class disjointness axioms. Our approach aims at leveraging the implicit knowledge embedded in LLMs, using prompt engineering to elicit this knowledge for classifying ontological disjointness. We validate our methodology on the DBpedia ontology, focusing on open-source LLMs. Our findings suggest that LLMs, when guided by effective prompt strategies, can reliably identify disjoint class relationships, thus streamlining the process of ontology completion without extensive manual input. For comprehensive disjointness enrichment, we propose a process that takes logical relationships between disjointness and subclass statements into account in order to maintain satisfiability and reduce the number of calls to the LLM. This work provides a foundation for future applications of LLMs in automated ontology enhancement and offers insights into optimizing LLM performance through strategic prompt design. Our code is publicly available on GitHub at https://github.com/n28div/llm-disjointness.
ISSN:2331-8422