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- ItemRepresenting Semantified Biological Assays in the Open Research Knowledge Graph(Cham : Springer, 2020) Anteghini, Marco; D'Souza, Jennifer; Martins dos Santos, Vitor A.P.; Auer, Sören; Ishita, Emi; Pang, Natalie Lee San; Zhou, LihongIn the biotechnology and biomedical domains, recent text mining efforts advocate for machine-interpretable, and preferably, semantified, documentation formats of laboratory processes. This includes wet-lab protocols, (in)organic materials synthesis reactions, genetic manipulations and procedures for faster computer-mediated analysis and predictions. Herein, we present our work on the representation of semantified bioassays in the Open Research Knowledge Graph (ORKG). In particular, we describe a semantification system work-in-progress to generate, automatically and quickly, the critical semantified bioassay data mass needed to foster a consistent user audience to adopt the ORKG for recording their bioassays and facilitate the organisation of research, according to FAIR principles.
- ItemEasy Semantification of Bioassays(Heidelberg : Springer, 2022) Anteghini, Marco; D’Souza, Jennifer; dos Santos, Vitor A. P. Martins; Auer, SörenBiological 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.