Multi-level ontology-based conceptual modeling

Carvalho, Victorio Albani de; Almeida, João Paulo A.; Fonseca, Claudenir M.; Guizzardi, Giancarlo

doi:10.1016/j.datak.2017.03.002

Cited by 45 publications

(33 citation statements)

References 17 publications

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“…A Role Mixin represents an anti-rigid and relationally dependent non-sortal, i.e., a dispersive type that aggregates properties that are common to different Roles (e.g., the type Customer that aggregates properties of individual customers and corporate customers) [8]. Although not prescribed in the original set of UFO endurant types, over the years, the notion of a Phase Mixin emerged as a useful notion that was found missing by different authors [11,4]. A Phase Mixin represents an anti-rigid and relationally independent nonsortal, i.e., a dispersive type that aggregates properties that are common to different Phases (e.g., the type Active Agent that aggregates properties of living people and active organizations).…”

Section: Background: Ufo and Ontoumlmentioning

confidence: 99%

Endurant Types in Ontology-Driven Conceptual Modeling: Towards OntoUML 2.0

Guizzardi

Fonseca

Benevides

et al. 2018

Conceptual Modeling

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For over a decade now, a community of researchers has contributed to the development of the Unified Foundational Ontology (UFO)-aimed at providing foundations for all major conceptual modeling constructs. This ontology has led to the development of an Ontology-Driven Conceptual Modeling language dubbed OntoUML, reflecting the ontological micro-theories comprising UFO. Over the years, UFO and OntoUML have been successfully employed in a number of academic, industrial and governmental settings to create conceptual models in a variety of different domains. These experiences have pointed out to opportunities of improvement not only to the language itself but also to its underlying theory. In this paper, we take the first step in that direction by revising the theory of types in UFO in response to empirical evidence. The new version of this theory shows that many of the meta-types present in OntoUML (differentiating Kinds, Roles, Phases, Mixins, etc.) should be considered not as restricted to Substantial types but instead should be applied to model Endurant Types in general, including Relator types, Quality types and Mode types. We also contribute a formal characterization of this fragment of the theory, which is then used to advance a metamodel for OntoUML 2.0. Finally, we propose a computational support tool implementing this updated metamodel.

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Section: Background: Ufo and Ontoumlmentioning

confidence: 99%

Endurant Types in Ontology-Driven Conceptual Modeling: Towards OntoUML 2.0

Guizzardi

Fonseca

Benevides

et al. 2018

Conceptual Modeling

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“…Without such flexibility, the designer would be forced to manually inspect the data sources and enrich the ontology off-line. The second issue is related to the need of meta-modeling constructs in the language used to specify the ontology [16][17][18][19]. Meta-modeling allows concepts and relations to be conceived as first-order citizens and to see them as individuals that are instances of other concepts, called meta-concepts.…”

Section: Introductionmentioning

confidence: 99%

Acquiring Ontology Axioms through Mappings to Data Sources

et al. 2019

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Although current languages used in ontology-based data access (OBDA) systems allow for mapping source data to instances of concepts and relations in the ontology, several application domains need more flexible tools for inferring knowledge from data, which are able to dynamically acquire axioms about new concepts and relations directly from the data. In this paper we introduce the notion of mapping-based knowledge base (MKB) to formalize the situation where both the extensional and the intensional level of the ontology are determined by suitable mappings to a set of data sources. This allows for making the intensional level of the ontology as dynamic as the extensional level traditionally is. To do so, we resort to the meta-modeling capabilities of higher-order description logics, in particular the description logic Hi(DL-Lite R ), which allows seeing concepts and relations as individuals, and vice versa. The challenge in this setting is to design efficient algorithms for answering queries posed to MKBs. Besides the definition of MKBs, our main contribution is to prove that answering instance queries posed to MKBs expressed in Hi(DL-Lite R ) can be done efficiently.

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“…The conceptual model (or ontology model) level comprises the meta types and the direct or indirect supertypes of all the concepts in the UO [17,18]. The concepts at this level are used to define the rest of the UO.…”

Section: The Conceptual Model Levelmentioning

confidence: 99%

The Universal Ontology: A Vision for Conceptual Modeling and the Semantic Web (Invited Paper)

Olivé

2017

Conceptual Modeling

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Multi-level ontology-based conceptual modeling

Cited by 45 publications

References 17 publications

Endurant Types in Ontology-Driven Conceptual Modeling: Towards OntoUML 2.0

Endurant Types in Ontology-Driven Conceptual Modeling: Towards OntoUML 2.0

Acquiring Ontology Axioms through Mappings to Data Sources

The Universal Ontology: A Vision for Conceptual Modeling and the Semantic Web (Invited Paper)

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