Research Interests: Information Systems, Computer Science, Applied Ontology, Enterprise Architecture, Conceptual Modelling, and 15 moreAccounting Information Systems, Information Systems Development, Knowledge Representation, Conceptual Modeling, Information Systems Analysis and Design, Logics for Knowledge Representation, Conceptual Modeling and Semantic Models, Complex Event Processing, Knowledge Representation and Reasoning, Information systems analysis and design methods, Conceptual Data Modeling, Description Logic, Information Systems Development And Management, Applied Ontologies, and Alloy Language
Research Interests: Information Systems, Computer Science, Information Science, Information Systems (Business Informatics), Ontology, and 15 moreApplied Ontology, Ontology (Computer Science), Conceptual Modelling, Formal Ontology, Semantic Web, Events, Knowledge Representation, Ontology Engineering, Conceptual Modeling, Ontologia, Conceptual Model, Event Modelling, Information systems models, Knowledge Representation and Reasoning, and Conceptual Data Modeling
This paper presents an analysis of capability-related concepts in three enterprise architecture (EA) frameworks for the defense domain (DoDAF, MODAF and NAF). In this analysis, we adopt an ontological account for capabilities based on the... more
This paper presents an analysis of capability-related concepts in three enterprise architecture (EA) frameworks for the defense domain (DoDAF, MODAF and NAF). In this analysis, we adopt an ontological account for capabilities based on the notion of dispositions as endurants; a key aspect of this account is that it includes both disposition universals and individuals, in line with Aristotle’s four-category ontology. We show how these foundations – which differ from the perdurantist foundations underlying the three defense frameworks – can serve to clarify semantic issues in the frameworks’ support for capabilities.
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1 Postgraduate student, Federal University of Espírito Santo, Ontology and Conceptual Modeling Research Group (NEMO) Av. Fernando Ferrari, 514, CT-VII, 29075-910, Vitória, Brazil E-mail: cristine.griffo@aluno.ufes.br 2 Professor, Federal... more
1 Postgraduate student, Federal University of Espírito Santo, Ontology and Conceptual Modeling Research Group (NEMO) Av. Fernando Ferrari, 514, CT-VII, 29075-910, Vitória, Brazil E-mail: cristine.griffo@aluno.ufes.br 2 Professor, Federal University of Espírito Santo, Ontology and Conceptual Modeling Research Group (NEMO) Av. Fernando Ferrari, 514, CT-VII, 29075-910, Vitória, Brazil E-mail: jpalmeida@inf.ufes.br, gguizzardi@inf.ufes.br
Research Interests: Constitutional Law, Ontology, Applied Ontology, Ontology (Computer Science), Ontology Development, and 15 morePhilosophy Of Law, Conceptual Modelling, Formal Ontology, Social Ontology, Legal Ontology, Formal Ontology (Philosophy), Legal Informatics, Ontology Engineering, Ontologies, Robert Alexy, Enterprise Computing, Domain Ontologies, Computer Law, Legal Ontologies, and Philosophy of Law
Assessing the quality of conceptual models is key to ensure that conceptual models can be used effectively as a basis for understanding, agreement and construction of information systems. This paper proposes an approach to assess... more
Assessing the quality of conceptual models is key to ensure that conceptual models can be used effectively as a basis for understanding, agreement and construction of information systems. This paper proposes an approach to assess conceptual models defined in OntoUML by transforming these models into specifications in the logic-based language Alloy. These Alloy specifications include the modal axioms of the theory underlying OntoUML, allowing us to validate the modal meta-properties representing ontological commitments of the OntoUML types and relations.
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Conceptual models are often built with techniques that propose a strict stratification of entities into two classification levels: a level of types (or classes) and a level of instances. Multi-level conceptual modeling extends the... more
Conceptual models are often built with techniques that propose a strict stratification of entities into two classification levels: a level of types (or classes) and a level of instances. Multi-level conceptual modeling extends the conventional two-level scheme by admitting that types can be instances of other types, giving rise to multiple levels of classification (individuals, classes, metaclasses, metametaclasses, and so on). As a result, multi-level models capture not only invariants about individuals, but also invariants about types themselves, which become regular elements of the domain of inquiry (first-class citizens). Despite the benefits of the multi-level approach, the vast majority of tools for conceptual modeling are still confined to the two-level scheme, and hence cannot accommodate multi-level entities. This paper proposes a transformation of multi-level to two-level models that preserves the semantics of the original multi-level model. We employ the systematic reific...
Research Interests: Information Systems, Computer Science, Software Engineering, Information Science, Information Systems (Business Informatics), and 15 moreApplied Ontology, Ontology (Computer Science), Conceptual Modelling, Metamodelling, Semantic Web, Knowledge Representation, Ontology Engineering, Conceptual Modeling, Ontologies, Ontologia, Conceptual Model, UML Class Diagram, Information systems models, Knowledge Representation and Reasoning, and Conceptual Data Modeling
Conceptual models are often built with techniques that propose a strict stratification of entities into two classification levels: a level of types (or classes) and a level of instances. Multi-level conceptual modeling extends the... more
Conceptual models are often built with techniques that propose a strict stratification of entities into two classification levels: a level of types (or classes) and a level of instances. Multi-level conceptual modeling extends the conventional two-level scheme by admitting that types can be instances of other types, giving rise to multiple levels of classification. Nevertheless, the vast majority of tools and techniques are still confined to the two-level scheme, and hence cannot be used for multi-level models directly. We show here how a multi-level model in ML2 can be transformed into a two-level specification in the formal modeling technique Alloy, thereby leveraging the Alloy analyzer to multi-level models.
Research Interests: Information Systems, Computer Science, Software Engineering, Information Science, Information Systems (Business Informatics), and 15 moreOntology, Applied Ontology, Ontology (Computer Science), Conceptual Modelling, Semantic Web, Knowledge Representation, Ontology Engineering, Conceptual Modeling, Ontologies, Ontologia, Conceptual Model, UML Class Diagram, Information systems models, Knowledge Representation and Reasoning, and Conceptual Data Modeling
Research Interests: Computer Science, Artificial Intelligence, Ontology, Applied Ontology, Semantic Web Technologies, and 15 moreComputational Logic, Conceptual Modelling, Semantic Web, Artificial Inteligence, Knowledge Representation, Multidisciplinary, Description logics, Ontologies, Ontología, Philosophy of Artificial Intelligence, Ontologia, Conceptual Model, Inteligencia artificial, Knowledge Representation and Reasoning, and Web Semántica
Research Interests: Computer Science, Semantic Web Technologies, Semantic Web technology - Ontologies, Semantic Web, Knowledge Representation, and 5 moreLogics for Knowledge Representation, Knowledge Representation and Reasoning, Web Semántica, Knowledge representation and reasonning, and Semantic Web for Health Care and Life Sciences
Research Interests: Computer Science, Applied Ontology, Enterprise Architecture, Event Management, Business Process Management, and 15 moreConceptual Modelling, Business Process Modeling, Business Information Systems, Data Modeling, Conceptual Modeling, Conceptual Modeling and Semantic Models, Complex Event Processing, Business Information technology, Business Informatics, Enterprise Information Systems, Business Information System, Conceptual Model, Business Process Modelling, Conceptual Data Modeling, and Business Information Management
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Research Interests: Information Systems, Computer Science, Software Engineering, Information Science, Management Information Systems, and 15 moreOntology, Ontology Development, Conceptual Modelling, Metamodelling, Library and Information Science, Formal Ontology, Accounting Information Systems, Ontology Engineering, Conceptual Modeling, Conceptual Modeling and Semantic Models, Information Systems Engineering, Metamodeling, Conceptual Data Modeling, Multi Level Modeling, and Business and Information Systems Engineering
Research Interests: Information Systems, Computer Science, Information Systems (Business Informatics), Management Information Systems, Applied Ontology, and 15 moreEnterprise Architecture, Ontology Development, Conceptual Modelling, Library and Information Science, Capability Approach, Ontology Engineering, Conceptual Modeling, Capability, Business Information technology, Business Informatics, Enterprise Information Systems, Business Information System, Capabilities, Resource, and Archimate
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Research Interests: Computer Science, Applied Ontology, Enterprise Architecture, Ontology (Computer Science), Capability Approach, and 13 moreEnterprise Modeling, Capability, Investment, enterprise Ontology, Business Strategy, IEEE, Capabilities Approach, Domain Ontologies, Enterprise modelling, Resource, Strategy Business, Archimate, and OntoUML
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Research Interests: Information Systems, Computer Science, Software Engineering, Formal Methods (Formal Verification), Information Systems (Business Informatics), and 12 moreOntology (Computer Science), Conceptual Modelling, Formal methods, Conceptual Modeling, Conceptual Modeling and Semantic Models, Model validation, Information Systems Engineering, Prey Choice Model, Conceptual Model, OntoUML, Lightweight Formal Methods, and Alloy Language
Research Interests: Computer Science, Formal Methods (Formal Verification), Applied Ontology, Ontology (Computer Science), Conceptual Modelling, and 10 moreSemantic Web technology - Ontologies, Model validation, Key words, Conceptual Model, Modal, Conceptual Data Modeling, OntoUML, Alloy Language, Conceptual, and Information System
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Research Interests: Information Systems, Computer Science, Information Systems (Business Informatics), Management Information Systems, Applied Ontology, and 15 moreEnterprise Architecture, Conceptual Modelling, Design Patterns, Business Information Systems, Semantic Web, Software Language Engineering, Enterprise Modeling, Business Informatics, Management Information System, Business Information System, Design Pattern, Domain Ontologies, Software Design Patterns, Organizational modeling, and Content Ontology Design Patterns
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While traditional approaches in business process modeling tend to focus on “how” the business processes are performed (adopting a behavioral description in which business processes are described in terms of procedural aspects), in... more
While traditional approaches in business process modeling tend to focus on “how” the business processes are performed (adopting a behavioral description in which business processes are described in terms of procedural aspects), in goal-oriented business process modeling, the proposals strive to extend traditional business process methodologies by providing a dimension of intentionality to business processes. One of the key difficulties in enabling one to model goal-oriented processes concerns the identification or elicitation of goals. This paper reports on a case study conducted in a Brazilian hospital, which obtained several goal models represented in i*/Tropos, each of which correspond to a business process also modeled in the scope of the study. NFR catalogues were helpful in goal elicitation, uncovering goals that did not come up during previous interviews prior to these catalogues’ use.
Research Interests: Requirements Engineering, Business Process Management, Business Rules & Processes, Business Process Modeling, Goal Oriented Requirements Engineering, and 9 moreSoftware Requirement Engineering, Non Functional Requirement, Requirements elicitation, Software Requirements, Requirements Analysis, Business Process, Requirements Engineering for Home Health Care Software Systems, Non Functional Requirements, and Goal oriented Modelling
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Research Interests: Information Systems, Computer Science, Management Information Systems, Ontology, Enterprise Architecture, and 15 moreTrust, Conceptual Modelling, Library and Information Science, Trusted Computing, Trust Management, Organizational Trust, Social Trust, Ontologies, Enterprise Architecture Management, Enterprise Engineering, Business Informatics, Conceptual Model, Conceptual Data Modeling, Enterprise modelling, and CoVid
Research Interests: Information Systems, Cognitive Science, Computer Science, Ontology, Applied Ontology, and 15 moreCrisis Management, Conceptual Modelling, Pervasive Computing, Context, Knowledge Representation, Ontologies, Context Awareness, Computational Cognitive Science, Ontología, Cognitive Computing, Ontologia, Conceptual Model, Knowledge Representation and Reasoning, Conceptual Data Modeling, and Pervasive and Ubiquitous Computing
The Unified Foundational Ontology (UFO-A) is a foundational ontology about endurants that has been built as a foundation for conceptual modeling, and which focuses on static aspects of endurants. In addition to UFO-A, we have the Unified... more
The Unified Foundational Ontology (UFO-A) is a foundational ontology about endurants that has been built as a foundation for conceptual modeling, and which focuses on static aspects of endurants. In addition to UFO-A, we have the Unified Foundational Ontology–Part B (UFO-B), a formal theory dealing with the interplay between endurants and the dynamic aspects of reality (e.g., events, processes, causation, dispositions, situations). Given the objective of characterizing this interplay between endurants and perdurants, these two ontologies are meant to form an integral whole. However, currently, they diverge in the way they approach modality. While UFO-A uses a general notion of alethic modality without committing to any notion of time, UFO-B is centered around temporal aspects. As an attempt to address this issue, we here define a translation of the axioms of UFO-A to FOL, and revisit an excerpt of UFO-B in order to accommodate a partial order of time points. With the goal of produci...
Research Interests: Computer Science, Artificial Intelligence, Ontology, Applied Ontology, Semantic Web Technologies, and 15 moreComputational Logic, Biomedical ontologies, Ontology (Computer Science), Conceptual Modelling, Formal Ontology, Semantic Web technology - Ontologies, Semantic Web, Knowledge Representation, Ontologies, Ontología, Philosophy of Artificial Intelligence, Ontologia, Conceptual Model, Inteligencia artificial, and Knowledge Representation and Reasoning
Enterprise Architecture (EA) promotes the establishment of a holistic view of the structure and way of working of an organization. One of the aspects covered in EA is associated with the organization’s “active structure”, which concerns... more
Enterprise Architecture (EA) promotes the establishment of a holistic view of the structure and way of working of an organization. One of the aspects covered in EA is associated with the organization’s “active structure”, which concerns “who” undertakes organizational activities. Several approaches have been proposed in order to provide a means for representing enterprise architectures, among which the ArchiMate, an EA modeling language. In this paper, we present a semantic analysis of the fragment of the ArchiMate metamodel related with the representation of active structure. In addition, we present a proposal to extend the metamodel based on a well-founded ontology for the organizational domain. Our objective is to enrich the language with important capabilities to represent organizational structures using a principled ontology-based approach.
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Environmental Quality Research (EQR) comprises many different methods, procedures and subdomains, often requiring the integration of heterogenous data from many sources. In this paper we show the first steps in building an ontology... more
Environmental Quality Research (EQR) comprises many different methods, procedures and subdomains, often requiring the integration of heterogenous data from many sources. In this paper we show the first steps in building an ontology network to support interoperability of EQR data. We present a bottom-up approach that begins by analyzing available data to capture relevant community concerns and then establish the domain coverage. We focus on identifying and reusing existent knowledge sources to cover the semantics of extant data.
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The Unified Foundational Ontology (UFO) was developed over the last two decades by consistently putting together theories from areas such as formal ontology in philosophy, cognitive science, linguistics, and philosophical logics. It... more
The Unified Foundational Ontology (UFO) was developed over the last two decades by consistently putting together theories from areas such as formal ontology in philosophy, cognitive science, linguistics, and philosophical logics. It comprises a number of micro-theories addressing fundamental conceptual modeling notions, including entity types and relationship types. The aim of this paper is to summarize the current state of UFO, presenting a formalization of the ontology, along with the analysis of a number of cases to illustrate the application of UFO and facilitate its comparison with other foundational ontologies in this special issue. (The cases originate from the First FOUST Workshop – the Foundational Stance, an international forum dedicated to Foundational Ontology research.)
Research Interests: Information Science, Ontology, Applied Ontology, Ontology (Computer Science), Ontology Development, and 15 moreConceptual Modelling, Formal Ontology, Philosophical Logic, Semantic Web technology - Ontologies, Semantic Web, Knowledge Representation, Multidisciplinary, Ontology Engineering, Conceptual Modeling, Ontologies, Ontología, Ontologia, Conceptual Model, Knowledge Representation and Reasoning, and Conceptual Data Modeling
Convergence is driving the uptake of new technologies on networking and service layers. The emerging new network infrastructure centered on 3GPP and IMS will have a horizontal layering with a good separation of control functions, media... more
Convergence is driving the uptake of new technologies on networking and service layers. The emerging new network infrastructure centered on 3GPP and IMS will have a horizontal layering with a good separation of control functions, media handling, networking issues, and service layers. This is a first step for fast service creations. In addition, we need a good tool chain for service creation as well as a rich execution environment. This paper describes the approach of the IST project SPICE with respect to Service Creation Environment and Service Execution Environment.
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The increasing volume and complexity of scientific research data associated with its semantic heterogeneity demands strategies to enable data integrated reuse. This is essential to improve global collaborations, in what has been called... more
The increasing volume and complexity of scientific research data associated with its semantic heterogeneity demands strategies to enable data integrated reuse. This is essential to improve global collaborations, in what has been called e-Science. A way to promote data integration is through the use of ontologies. Ontologies can play the role of a shared conceptualization, providing a common semantic background for data interpretation. In the case of scientific research, particularly empirical research, there are many concepts related to research activities that are general, despite any specific domain in which they may occur. Thus, they can be represented by means of a core ontology. In this paper, we propose the design of a core ontology to deal with research activities (e.g., sampling and measurement). As the concepts used are neutral with respect to different application domains, they can be reused to build ontologies for specific research domains, speeding up the development pro...
Conceptual models are often built with techniques which propose a strict stratification of entities into two classification levels: a level of types (or classes) and a level of instances. Despite that, there are several situations in... more
Conceptual models are often built with techniques which propose a strict stratification of entities into two classification levels: a level of types (or classes) and a level of instances. Despite that, there are several situations in which domains of inquiry transcend the conventional two-level stratification and domain experts use types of types (or categories of categories) to articulate their conceptualizations. In these settings, types are instances of other types and multiple levels of classification can be identified (individuals, classes, metaclasses, metametaclasses, and so on), characterizing what is now called “multi-level modeling”. Over the last years, we have worked out a foundational theory for multi-level modeling (dubbed MLT), whose aim is to clarify the basic elements of multi-level conceptual modeling. This paper describes the development of this theory, and reports on some of its applications, namely: the detection of (thousands of) occurrences of anti-patterns in...
Research Interests: Information Systems, Information Science, Applied Ontology, Semantic Web Technologies, Ontology (Computer Science), and 14 moreConceptual Modelling, Metamodelling, Formal Ontology, Semantic Web technology - Ontologies, Semantic Web, Conceptual Modeling, Ontologies, Conceptual Modeling and Semantic Models, Semantic Computing, Conceptual Model, Metamodeling, Domain Ontologies, Conceptual Data Modeling, and Multi Level Modeling
platform characteristics and the characteristics of modelling languages adopted for a design are interrelated. For example, let us suppose a designer chooses to use SDL [41] to represent platformindependent designs. This language provides... more
platform characteristics and the characteristics of modelling languages adopted for a design are interrelated. For example, let us suppose a designer chooses to use SDL [41] to represent platformindependent designs. This language provides the “agent” structuring construct: an “agent” is an entity that can exhibit reactive behaviour and communicates with other “agents” by exchanging “signals” asynchronously. If a designer models application parts as “agents” that interact with other application parts through “signals”, this use of SDL implicitly defines an abstract platform that supports reliable asynchronous message exchange. One might be tempted to believe that all relevant characteristics of a design’s abstract platform can be derived from the concepts underlying the modelling language adopted for the design. However, abstract platform characteristics may depend on restrictions on the use of particular constructs in a modelling language or the use of certain modelling styles or pa...
