Reports of the Death of Knowledge Engineering Are Greatly Exaggerated
KGC 2025
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44m
Biomedical researchers generate experimental data at prodigious rates. There are widespread dreams that these data can be studied and re-explored to enable third parties to verify the published claims of the original investigators and to make new discoveries themselves through secondary analyses. Biomedical data, however, are rarely archived in a condition that makes it possible to understand what other investigators have done and to re-explore their data. Knowledge graphs, however, offer the opportunity to encode the preferences of scientific communities regarding how they would like to format their data and how shared data should be interpreted. Building such graphs requires good old fashioned knowledge engineering—an art form that many of us believe began to fade with the advent of modern machine learning. Making scientific data “AI ready,” however, requires AI less than it does blue-collar knowledge engineering. In this talk, I will discuss the opportunity for data reuse in science, and the role of knowledge graphs and knowledge engineering in making it happen.
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