Easy Semantification of Bioassays
This work addresses the need for efficient data integration in biological knowledge bases, offering a strong benchmark for automated semantification, though it is incremental as it builds on existing methods.
The paper tackled the problem of automatically semantifying biological assays by comparing labeling versus clustering methods, finding that a clustering solution significantly outperforms a deep neural network state-of-the-art labeling approach with an F1 score of nearly 83%.
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.