Through high-quality foundational data management, where ontologies play a crucial role, an organization can be agile enough to adapt to and make use of state-of-the-art technologies such as large language models (LLM). An LLM is a sophisticated, generative, artificial intelligence (AI) model designed to understand and generate human-like text. Trained on monumental amounts of data, LLMs are designed to generate coherent and contextually relevant responses. LLMs are, therefore, great at language-based tasks that allow them to draw on their learned knowledge of textual patterns, such as summarization, generation, aggregation, translation, and programming assistance.
Ontologies capture human knowledge in a format that is computationally friendly; allowing for analysis to be completed with up-to-date knowledge of the subject matter expert (SME) in a scalable fashion. A key use case of ontologies, or more specifically ontology-derived standards, is the tagging and management of data. Whether that be structured, unstructured, internal, or external, by aligning your data to standards, it becomes more Findable, Accessible, Interoperable, and Reusable (FAIR). As a result, information retrieval tasks can be enhanced, and by representing previously unstructured data in a structured, semantic fashion, inference and extraction of insights can be expedited.
LLMs can help uncover ‘knowledge’, yet ontologies are needed to capture that for future use. While various technologies can support the semi-automated curation of ontologies, human validation is crucial for candidate classes, terms, and relationships. Transforming LLM output into an ontology through a lightweight model enables versatility and reuse in downstream applications.
Even if AI could create a flawless ontology, it is crucial to recognize that the value of a standard lies in its consensus among humans. While AI may generate a well-structured ontology, it may well struggle with nuanced distinctions, and diverge from what others use. The human in the loop is vital.
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