OLIVE: Ontology Learning With Integrated Vector Embeddings

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Abstract
The traditional approach to ontology development is characterized by its labor-intensive nature, requiring extensive effort and domain expertise to define intricate structures, relationships, and concepts accurately. This study proposes a paradigm shift in ontology development by harnessing the capabilities of large language models (LLMs). This methodology entails the creation of an interactive interface that empowers users to query LLMs using prompts, facilitating the retrieval of pertinent information with ease. Subsequent analysis of this information allows for identifying key relationships, which are then transformed into graph structures using the Web Ontology Language (OWL). The outcome of this process is OLIVE—an ontology development workflow engineered to streamline manual efforts and minimize the risk of errors.
Funding Information
  • Office of Multidisciplinary Activities (2333532)

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