Taxonomies and Ontologies Transforming Knowledge Management
The first is by standardizing the terminology and accompanying business concepts found in these metadata descriptions. The second is by making them machine-readable, so they can enhance deployments of language models for addressing this problem. For Musen’s application, language models can automate the metadata descriptions of research projects and potentially rewrite existing descriptions that adhere to the standards Musen mentioned. The former would make significant headway in solving the data annotation challenge; the latter could possibly eradicate it altogether.
The Foundation of Knowledge Management
Granted, there are emergent applications of taxonomies and ontologies applicable to KM, many of which are predicated on exploiting the utility of language models. However, the relationship between these two constructs and KM is long-standing and predates today’s preoccupation with GenAI. Even in the biomedical field, some of the most frequently recurring uses of taxonomies and ontologies have nothing to do with generative models or even scientific research. As Musen noted, ontologies endowed with taxonomies are the basis for medical billing systems, “which are required to clarify what was done to someone in the healthcare system. And that’s an example in which an ontology is used thousands of times every hour.”
The overarching importance of taxonomies and ontologies, however, is that the machine-readable ones supported in standards-based environments can be connected to other knowledge via semantic graph frameworks. As such, they provide a KM blueprint that will almost surely endure, notwithstanding any future gains made by machine learning. “Knowledge graphs give us the opportunity to link those experimental findings with general knowledge,” Musen stipulated. “We can take the results of our experiments and use ontologies to describe them in precise and clear terms and link those results to generate a knowledge graph about conditions we’re studying and the problems we’re trying to investigate.”
The long history of KM’s reliance on taxonomies and ontologies is adding new, transformative possibilities as practitioners explore technological developments with AI.
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