ABSTRACT
The pandemic outbreak of COVID-19 has allowed the proliferation of an unprecedented amount of data that must be organized and connected in a way that allows its efficient management. Nevertheless, the speed at which all of this knowledge is being generated has highlighted the shortcomings of the research community in creating well-organized, standardized, and structured databases. Despite the efforts of the community to develop advanced integrative platforms such as CovidGraph, we have identified some limitations when using these solutions that we think are derived from the lack of a sound ontological schema to guide the collection, standardization, and integration of data. This work explores the advantages and disadvantages for the final user of building advanced information systems using a Model Driven Development approach to integrate heterogeneous and complex data using an ontological background as a basis. As a proof of concept, we built a database (CovProt) to integrate data about different aspects of SARS-CoV-2 using this approach, we analyzed the advantages and disadvantages of using this approach compared to CovidGraph by performing a set of queries in CovProt and CovidGraph, and finally, we compared the structure and redundancy of the retrieved data. Author
ABSTRACT
Inspired by the need to understand the genomic aspects of COVID-19, the Viral Conceptual Model captures and represents the sequencing of viruses. Although the model has already been successfully used, it should have a strong ontological foundation to ensure that it can be consistently applied and expanded. We apply an ontological analysis of the Viral Conceptual Model, using OntoUML, to unpack and identify its core components. The analysis illustrates the feasibility of bringing ontological clarity to complex models. The process of revealing the ontological semantics of a data structuring model provides a fundamental type of explanation for symbolic models, including conceptual models. © 2021, Springer Nature Switzerland AG.