The Common Ontology of Value and Risk

Sales, Tiago Prince; Baião, Fernanda; Guizzardi, Giancarlo; Almeida, João Paulo A.; Guarino, Nicola; Mylopoulos, John

doi:10.1007/978-3-030-00847-5_11

Cited by 59 publications

(64 citation statements)

References 16 publications

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“…A large corpus of literature exists on the use of formal ontologies to formalize knowledge in the disaster management domain. In the Hazard, Vulnerability Risk (HVR) portion of the disaster management cycle, work has been done to conceptualize hazard [12], vulnerability [13] and risk [14][15][16] concepts. Additionally, surveys conducted by [5,6] show that there exists several formal ontologies and vocabularies for capturing, representing and integrating heterogeneous data in the emergency management sub-domain.…”

Section: Ontologies In the Disaster Management Domainmentioning

confidence: 99%

“…The disaster risk reduction (DRR) domain comprises key concepts namely; disaster, risk, SN Computer Science hazard, vulnerability, and resilience [28] that are critical to this observation. Sales et al [16] provided a conceptualization of "pure" risk assessment for which vulnerability is an essential component. Various frameworks in the literature [2,29,30] define vulnerability as a function of resilience, resistance or susceptibility concepts.…”

Section: Ontology Representation Of Disaster Risk Knowledgementioning

confidence: 99%

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A Pattern Driven Approach to Knowledge Representation in the Disaster Domain

et al. 2020

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Access to integrated disaster-related data through querying is still a problem due to associated semantic barriers. The disaster domain largely relies on the top-down approach of ontology development. This limits reuse due to associated commitments and complex alignments within ontologies. Therefore, there is a need to utilize a bottom-up approach that reuses patterns for representing disaster knowledge. To bridge the availability gap of patterns for representing disaster knowledge, this study identifies existing and emerging patterns for reuse while organizing disaster data from multiple sector stakeholders. Based on the eXtreme Design (XD) methodology and key informant interviews, competency questions (CQs) were elicited from domain stakeholders. The CQs are matched with existing patterns from other contexts. Emerging patterns (e.g the Event Classification and Quality Dependence Description for Objects) are also developed for CQs not captured and subsequently tested using SPARQL queries characterising the CQs. It is in this context that this paper presents a characterisation of disaster risk knowledge using CQs and corresponding patterns (reusable and emerging) covering the knowledge. Accordingly, we illustrate a pattern-driven use case to organise drought hazard data for early warning purposes. This provides a powerful use case for adopting a pattern-based approach to knowledge representation in the disaster domain.

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Section: Ontologies In the Disaster Management Domainmentioning

confidence: 99%

Section: Ontology Representation Of Disaster Risk Knowledgementioning

confidence: 99%

A Pattern Driven Approach to Knowledge Representation in the Disaster Domain

et al. 2020

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“…The Common Ontology of ValuE and Risk (COVER) [21] formalizes a particular sense in which the term value is used, namely that of use value. Briefly put, use value is the quality that summarizes a utility assessment of an object or experience from the perspective of a given subject.…”

Section: Cover: Common Ontology Of Value and Riskmentioning

confidence: 99%

“…To address this ambiguity, a value modeling approach for ArchiMate should be based on a proper ontological theory, which provides adequate real-world and formal semantics for such a language's vocabulary [10]. In particular, we make use of the concepts and relations defined in the Common Ontology of ValuE and Risk (COVER) [21] (Section 2.2), a novel well-founded reference ontology that explains value and risk as two ends of the same spectrum. COVER is grounded on several theories from marketing, service science, strategy and risk management.…”

Section: Introductionmentioning

confidence: 99%

A Pattern Language for Value Modeling in ArchiMate

Sales

Roelens

Poels

et al. 2019

Advanced Information Systems Engineering

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“…Future work regards the development of the OntoUML module to articulate specific types of preferences and the integration of the preference module with other module designed to treat important concepts in economics, e.g. economic competition [26] and risk [27]. Moreover, we are interested in providing a formalisation of this view of preference in a logic-based language to integrate it with foundational ontologies such as UFO or DOLCE [9,2] and to study reasoners for the preference module.…”

Section: Types Of Preference Relationsmentioning

confidence: 99%

Towards an Ontological Modelling of Preference Relations

Porello

Guizzardi

2018

AI*IA 2018 – Advances in Artificial Intelligence

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Preference relations are intensively studied in Economics, but they are also approached in AI, Knowledge Representation, and Conceptual Modelling, as they provide a key concept in a variety of domains of application. In this paper, we propose an ontological foundation of preference relations to formalise their essential aspects across domains. Firstly, we shall discuss what is the ontological status of the relata of a preference relation. Secondly, we investigate the place of preference relations within a rich taxonomy of relations (e.g. we ask whether they are internal or external, essential or contingent, descriptive or nondescriptive relations). Finally, we provide an ontological modelling of preference relation as a module of a foundational (or upper) ontology (viz. OntoUML). The aim of this paper is to provide a sharable foundational theory of preference relation that foster interoperability across the heterogeneous domains of application of preference relations.

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The Common Ontology of Value and Risk

Cited by 59 publications

References 16 publications

A Pattern Driven Approach to Knowledge Representation in the Disaster Domain

A Pattern Driven Approach to Knowledge Representation in the Disaster Domain

A Pattern Language for Value Modeling in ArchiMate

Towards an Ontological Modelling of Preference Relations

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