  Biological data and knowledge bases increasingly rely on Semantic Web
technologies and the use of knowledge graphs for data integration, retrieval
and federated queries. We propose a solution for automatically semantifying
biological assays. Our solution contrasts the problem of automated
semantification as labeling versus clustering where the two methods are on
opposite ends of the method complexity spectrum. Characteristically modeling
our problem, we find the clustering solution significantly outperforms a deep
neural network state-of-the-art labeling approach. This novel contribution is
based on two factors: 1) a learning objective closely modeled after the data
outperforms an alternative approach with sophisticated semantic modeling; 2)
automatically semantifying biological assays achieves a high performance F1 of
nearly 83%, which to our knowledge is the first reported standardized
evaluation of the task offering a strong benchmark model.
