Ontological Unpacking as Explanation: The Case of the Viral Conceptual Model

Guizzardi, Giancarlo; Bernasconi, Anna; Pastor, Óscar

doi:10.1007/978-3-030-89022-3_28

Cited by 13 publications

(16 citation statements)

References 15 publications

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“…In [20] (and extended here), we argue that a conceptual model should be able to provide conceptual clarification and unambiguous communication regarding the nature of entities and their connections, which are assumed to exist in a given domain. This is the ontological function of a conceptual model.…”

Section: Introductionmentioning

confidence: 86%

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: Introductionmentioning

confidence: 86%

Semantic interoperability: ontological unpacking of a viral conceptual model

2022

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“…Risk treatment options include: "(a) avoiding the risk by deciding not to start or continue with the activity that gives rise to the risk; (b) taking or increasing the risk in order to pursue an opportunity; (c) removing the risk source; (d) changing the likelihood; (e) changing the consequences; (f) sharing the risk with another party or parties (including contracts and risk financing); and (g) retaining the risk by informed decision" [16]. ROSE can be seen as a way of ontologically unpacking [15] this notion of risk treatment, according to the risk treatment options, through an ontological analysis based on the Unified Foundational Ontology (UFO) and its general-purpose conceptual modeling language OntoUML [12].…”

Section: Requirements For a Reference Ontology Of Securitymentioning

confidence: 99%

An Ontology of Security from a Risk Treatment Perspective

Oliveira

Sales²,

Baratella³

et al. 2022

Lecture Notes in Computer Science

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In Risk Management, security issues arise from complex relations among objects and agents, their capabilities and vulnerabilities, the events they are involved in, and the value and risk they ensue to the stakeholders at hand. Further, there are patterns involving these relations that crosscut many domains, ranging from information security to public safety. Understanding and forming a shared conceptualization and vocabulary about these notions and their relations is fundamental for modeling the corresponding scenarios, so that proper security countermeasures can be devised. Ontologies are instruments developed to address these conceptual clarification and terminological systematization issues. Over the years, several ontologies have been proposed in Risk Management and Security Engineering. However, as shown in recent literature, they fall short in many respects, including generality and expressivity -the latter impacting on their interoperability with related models. We propose a Reference Ontology for Security Engineering (ROSE) from a Risk Treatment perspective. Our proposal leverages on two existing Reference Ontologies: the Common Ontology of Value and Risk and a Reference Ontology of Prevention, both of which are grounded on the Unified Foundational Ontology (UFO). ROSE is employed for modeling and analysing some cases, in particular providing clarification to the semantically overloaded notion of Security Mechanism.

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“…For example, if we know something is an event, we know it has objects as participants, it brings about changes in the world, it unfolds in time, it has a cause (a disposition or set of dispositions), etc. Once we analyze that event, then we can codify/axiomatize all those properties in a way that machines derive from (for instance) a type of ontology and can predictably execute (Guizzardi et al, 2021 ).…”

Section: In Science We Should Communicate Meaning As Precisely As Pos...mentioning

confidence: 99%

FAIR Digital Twins for Data-Intensive Research

Schultes¹,

Roos

Santos

et al. 2022

Front. Big Data

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Although all the technical components supporting fully orchestrated Digital Twins (DT) currently exist, what remains missing is a conceptual clarification and analysis of a more generalized concept of a DT that is made FAIR, that is, universally machine actionable. This methodological overview is a first step toward this clarification. We present a review of previously developed semantic artifacts and how they may be used to compose a higher-order data model referred to here as a FAIR Digital Twin (FDT). We propose an architectural design to compose, store and reuse FDTs supporting data intensive research, with emphasis on privacy by design and their use in GDPR compliant open science.

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Ontological Unpacking as Explanation: The Case of the Viral Conceptual Model

Cited by 13 publications

References 15 publications

Semantic interoperability: ontological unpacking of a viral conceptual model

Semantic interoperability: ontological unpacking of a viral conceptual model

An Ontology of Security from a Risk Treatment Perspective

FAIR Digital Twins for Data-Intensive Research

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