Semantic interoperability: ontological unpacking of a viral conceptual model

Abstract

Background: Genomics and virology are unquestionably important, but complex, domains being investigated by a large number of scientists. The need to facilitate and support work within these domains requires sharing of databases, although it is often difficult to do so because of the different ways in which data is represented across the databases. To foster semantic interoperability, models are needed that provide a deep understanding and interpretation of the concepts in a domain, so that the data can be consistently interpreted among researchers.

Results: In this research, we propose the use of conceptual models to support semantic interoperability among databases and assess their ontological clarity to support their effective use. This modeling effort is illustrated by its application to the Viral Conceptual Model (VCM) that captures and represents the sequencing of viruses, inspired by the need to understand the genomic aspects of the virus responsible for COVID-19. For achieving semantic clarity on the VCM, we leverage the "ontological unpacking" method, a process of ontological analysis that reveals the ontological foundation of the information that is represented in a conceptual model. This is accomplished by applying the stereotypes of the OntoUML ontology-driven conceptual modeling language.As a result, we propose a new OntoVCM, an ontologically grounded model, based on the initial VCM, but with guaranteed interoperability among the data sources that employ it.

Conclusions: We propose and illustrate how the unpacking of the Viral Conceptual Model resolves several issues related to semantic interoperability, the importance of which is recognized by the "I" in FAIR principles. The research addresses conceptual uncertainty within the domain of SARS-CoV-2 data and knowledge.The method employed provides the basis for further analyses of complex models currently used in life science applications, but lacking ontological grounding, subsequently hindering the interoperability needed for scientists to progress their research.

Keywords: COVID-19; Conceptual modeling; OntoUML; Ontological analysis; SARS-CoV-2; Viral genome.

Fig. 1

The Viral conceptual model (VCM), from Bernasconi et al. [12]

Fig. 2

Diagram representing the current proposed solutions for semantic interoperability of COVID-19-related information

Fig. 3

Overview of a part of OntoUML stereotypes, with their description and examples taken from the proposed OntoVCM

Fig. 4

OntoVCM; modules are clustered by background color: Viral Infection (blue), Tissue Sampling (pink), Virus Sequencing (gray), Virus Sequence Publication (orange), Virus Sequence Annotation (green). A more readable version of this figure is available at [61]

Fig. 5

Left: VCM excerpt concerning the biological sample from which the infected tissue is extracted. Right: OntoVCM Tissue Sampling module

Fig. 6

Left: Excerpt of the original VCM technical perspective. Right: OntoVCM Virus Sequencing module

Fig. 7

Left: VCM excerpt concerning the sequence with its variants on the nucleotide and amino acid levels. Right: OntoVCM Virus Sequence Annotation module

Fig. 8

Attributes describing SARS-CoV-2 sequences, after our attempt to assign them to semantic modules described by the OntoVCM (see colored legend)