An Ontological Characterization of a Conceptual Model of the Human Genome

S., Alberto García; Guizzardi, Giancarlo; Pastor, Oscar; Bernasconi, Anna

doi:10.1007/978-3-031-07481-3_4

Cited by 7 publications

(9 citation statements)

References 13 publications

(15 reference statements)

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“…We have already been applying our method of "ontological unpacking" to other complex domains and, in particular, other sub-domains in the life sciences including a conceptual model of the human genome. The work described in [90] presents the first results of this initiative with a focus on biological pathways that describes the chemical reactions that explain the different molecular processes in the human body. Such results will be complemented with efforts to address other dimensions of human genomic information including: a structural view (composition of transcribable chromosome elements); a variation view (types of changes that may occur in the genome); a transcription view (process of moving from DNA to RNAs); a proteome view (characterizing proteins structure and properties); and a bibliography and data sources view (identifying relevant information related to sources of valid information, such as KEGG [91] or Reactome [92]).…”

Section: Discussionmentioning

confidence: 99%

Semantic interoperability: ontological unpacking of a viral conceptual model

2022

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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.

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Section: Discussionmentioning

confidence: 99%

Semantic interoperability: ontological unpacking of a viral conceptual model

2022

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“…This is a process of explanation (of the ontology underlying a certain description) that is called ontological unpacking. The process of unpacking that we have employed here is described in [3,9,16]. In this sense, our proposed schema is a representation of the data ontology behind the FAIR principles.…”

Section: Ontouml Fair Principles Schemamentioning

confidence: 99%

“…In other words, the modeling patterns constituting the language reflect the axiomatic micro-theories in UFO [27]. As a proxy for ontological analysis, OntoUML has proven to be a very effective support for the explanation of complex domains [3,9,16], because it can improve the understandability of technical concepts over traditional conceptual models [8,32].…”

Section: Introductionmentioning

confidence: 99%

Ontological Representation of FAIR Principles: A Blueprint for FAIRer Data Sources

Bernasconi¹,

S.²,

Guizzardi³

et al. 2023

Lecture Notes in Computer Science

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Guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of datasets, known as FAIR principles, were introduced in 2016 to enable machines to perform automatic actions on a variety of digital objects, including datasets. Since then, the principles have been widely adopted by data creators and users worldwide with the 'FAIR' acronym becoming a common part of the vocabulary of data scientists. However, there is still some controversy on how datasets should be interpreted since not all datasets that are claimed to be FAIR, necessarily follow the principles. In this research, we propose the OntoUML FAIR Principles Schema, as an ontological representation of FAIR principles for data practitioners. The work is based on OntoUML, an ontologically well-founded language for Ontology-driven Conceptual Modeling. OntoUML is a proxy for ontological analysis that has proven effective in supporting the explanation of complex domains. Our schema aims to disentangle the intricacies of the FAIR principles' definition, by resolving aspects that are ambiguous, under-specified, recursively-specified, or implicit. The schema can be considered as a blueprint, or a template to follow when the FAIR classification strategy of a dataset must be designed. To demonstrate the usefulness of the schema, we present a practical example based on genomic data and discuss how the results provided by the OntoUML FAIR Principles Schema contribute to existing data guidelines.

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“…Specifically, we consider the part describing human metabolic pathways. The original schema was conceived using UML; in our recent work [3] we performed an ontological analysis exercise, producing the corresponding OntoUML version. These two models are object of the experiment thereon described.…”

Section: Introductionmentioning

confidence: 99%

“…Its UML schema includes five modules, describing the structure of the human genome, protein synthesis, changes in the sequence referring to a reference sequence, information and sources related to the elements of the conceptual schema, and human metabolic pathways. Prior work on ontological unpacking [3] of the schema focused on a relevant portion of the last view; that is, on metabolic pathways. We sought to understand the impact of ensuring ontological clarity in the concepts employed.…”

Section: Introductionmentioning

confidence: 99%

An Initial Empirical Assessment of an Ontological Model of the Human Genome

Bernasconi²,

Guizzardi³

et al. 2022

Lecture Notes in Computer Science

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Conceptual modeling is used to model application domains for which an information system is needed. One of the most complex domains to which conceptual modeling has been applied is that of the human genome. Due to its complexity, its understanding is often left to domain experts. Conceptual models represent genomics-related concepts, with various purposes, including domain clarification or data structures design for facilitating data integration. However, traditional conceptual models, which might be expressed, for example, with UML, may not be appropriate for properly explaining such a complex domain, thus requiring an additional layer to ground the model on well-accepted ontological foundations. To achieve this result, an "ontological unpacking" method has been proposed that uses OntoUML as a visual formalism. In this research, we carry out an empirical study to compare the two mentioned representations. The study involved a small group of participants, who responded to a set of questions by reading either a UML model or its related OntoUML unpacked version; the results enabled us to assess their understanding of the domain. We aim to initiate a practical evaluation framework to assess the effectiveness, efficiency and user beliefs of models derived by ontologically unpacking traditional conceptual models. The results of the analysis provide the basis for a broader assessment.

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An Ontological Characterization of a Conceptual Model of the Human Genome

Cited by 7 publications

References 13 publications

Semantic interoperability: ontological unpacking of a viral conceptual model

Semantic interoperability: ontological unpacking of a viral conceptual model

Ontological Representation of FAIR Principles: A Blueprint for FAIRer Data Sources

An Initial Empirical Assessment of an Ontological Model of the Human Genome

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