In the past decades, the Unified Foundational Ontology (UFO) has played an important role in supporting the development of ontologies in academic and business settings, being employed to represent widely diverse domains. In this period, a... more
In the past decades, the Unified Foundational Ontology (UFO) has played an important role in supporting the development of ontologies in academic and business settings, being employed to represent widely diverse domains. In this period, a dedicated community of researchers has worked to support UFO and its representation language, OntoUML, by creating the OntoUML Lightweight Editor (OLED). Now that a new version of OntoUML is available, the need for up-to-date tool support has exposed the limitations of OLED, its development context, and the difficulties of bringing research contributions to the hands of modelers. To tackle these issues, this paper reflects upon the experiences of this community taking into consideration the goals of researchers (as developers) and modelers to devise a new microserviceoriented modeling infrastructure for OntoUML, called OntoUML as a Service (OaaS). This infrastructure supports future practical contributions to the language with a focus on lowering the entry barrier for the development new contributions and enabling an easier deployment to modelers. The paper also discusses the details of implementing OaaS through a number of projects that currently implement this infrastructure.
Research Interests:
Validating and debugging conceptual models is a very time-consuming task. Though separate software tools for model validation and machine learning are available, their integration for an automated support of the debugging-validation... more
Validating and debugging conceptual models is a very time-consuming task. Though separate software tools for model validation and machine learning are available, their integration for an automated support of the debugging-validation process still needs to be explored. The synergy between model validation for finding intended/unintended conceptual models instances and machine learning for suggesting repairs promises to be a fruitful relationship. This paper provides a preliminary description of a framework for an adequate automatic support to engineers and domain experts in the proper design of a conceptual model. By means of a running example, the analysis will focus on two main aspects: i) the process by which formal, tool-supported methods can be e↵ectively used to generate negative and positive examples, given an input conceptual model; ii) the key role of a learning system in uncovering error-prone structures and suggesting conceptual modeling repairs.
Research Interests: Information Systems, Software Engineering, Information Science, Artificial Intelligence, Information Systems (Business Informatics), and 15 moreOntology, Applied Ontology, Machine Learning, Ontology (Computer Science), Conceptual Modelling, Semantic Web technology - Ontologies, Inductive Logic Programming, Artificial Inteligence, Ontology Engineering, Conceptual Modeling, Philosophy of Artificial Intelligence, Ontologia, Conceptual Model, Inteligencia artificial, and Conceptual Data Modeling
In recent years, there has been a growing interest, within the financial sector, in the adoption of ontology-based conceptual models to make the nature of conceptualizations explicit, as well as to safely establish the correct relations... more
In recent years, there has been a growing interest, within the financial sector, in the adoption of ontology-based conceptual models to make the nature of conceptualizations explicit, as well as to safely establish the correct relations between them, thereby supporting semantic interoperability. Despite the wide number of efforts to create a unified view of the reality related to economic and financial domains, no comprehensive enough formal model has been developed to, on one hand, accurately describe the semantics regarding the world of money and currencies and, on the other hand, differentiate them from virtual currencies of which cryptocurrencies are the most significant representative. This research aims at tackling these questions by conducting an ontological analysis of money and related concepts, grounded in the Unified Foundational Ontology, based on a literature review of the most relevant economic theories, and considering recent innovations in the financial industry.
Research Interests: Information Systems, Finance, Economics, Ontology, Philosophy of Economics (Philosophy), and 15 moreApplied Ontology, Ontology (Computer Science), Conceptual Modelling, Philosophy Of Economics, Formal Ontology, Social Ontology, Accounting Information Systems, Theories of Money, Ontology Engineering, Ontología, Ontologia, Conceptual Model, Philosophy of Economics and Accounting, Accounting and Financial Information systems, and Cryptocurrency
Trust is widely acknowledged as the cornerstone of relationships in social life. But what makes an agent trust a person, a resource or an organization? Which characteristics should a trustee have in order to be considered trustwor-thy?... more
Trust is widely acknowledged as the cornerstone of relationships in social life. But what makes an agent trust a person, a resource or an organization? Which characteristics should a trustee have in order to be considered trustwor-thy? The importance of understanding trust in organizations has motivated us to investigate the representation of trust concerns in enterprise models. Based on a well-founded reference ontology of trust, we propose a pattern language for trust modeling in ArchiMate. We present a first iteration of the design cycle, which includes the development of the pattern language and its demonstration by means of a realistic case study about trust in a COVID-19 data repository.
Research Interests: Information Systems, Management Information Systems, Ontology, Enterprise Architecture, Trust, and 15 moreOntology (Computer Science), 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-19
In recent years, there has been a growing interest in the use of reference conceptual models to capture information about complex and sensitive business domains (e.g., finance, healthcare, space). These models play a fundamental role in... more
In recent years, there has been a growing interest in the use of reference conceptual models to capture information about complex and sensitive business domains (e.g., finance, healthcare, space). These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore , it is essential that domain experts are able to understand and reason with their content. In other words, it is important for these reference conceptual models to be cognitively tractable. This paper contributes to this goal by proposing a model clustering technique that leverages the rich semantics of ontology-driven conceptual models (ODCM). In particular, the technique employs the notion of Relational Context to guide automated model breakdown. Such Relational Contexts capture all the information needed for understanding entities "qua players of roles" in the scope of an objectified (reified) relationship (relator).
Research Interests: Information Systems, Software Engineering, Information Science, Management Information Systems, Applied Ontology, and 15 moreOntology (Computer Science), Conceptual Modelling, Semantic Web technology - Ontologies, Semantic Web, Ontology Engineering, Conceptual Modeling, Ontologies, Ontología, Business Informatics, Ontologia, Conceptual Model, Modularity, Frame Semantics, Conceptual Data Modeling, and Web Semántica
Trust is a key component of relationships in social life. It is commonly argued that trust is the "glue" that holds families, societies, organizations and companies together. In the literature trust is frequently considered as a strategic... more
Trust is a key component of relationships in social life. It is commonly argued that trust is the "glue" that holds families, societies, organizations and companies together. In the literature trust is frequently considered as a strategic asset for organizations. Having a clear understanding of the notion of trust and its components is paramount to both trust assessment and trust management. Although much progress has been made to clarify the ontological nature of trust, the term remains overloaded and there is not yet a shared or prevailing, and conceptually clear notion of trust. In this paper we address this issue by means of an in-depth ontological analysis of the notion of trust, grounded in the Unified Foundational Ontology. As a result, we propose a concrete arti-fact, namely, the Reference Ontology for Trust, in which we characterize the general concept of trust and distinguish between two types of trust, namely, social trust and institution-based trust. We also represent the emergence of risk from trust relations. In addition, we make a comparative analysis of our Reference Ontology to other trust ontologies. To validate and demonstrate the contribution of our approach, we apply it to model two application examples.
Research Interests: Information Systems, Information Science, Information Systems (Business Informatics), Ontology, Applied Ontology, and 15 moreTrust, Ontology (Computer Science), Trust Theory (Evolution of cooperation), Conceptual Modelling, Philosophy Of Economics, Formal Ontology, Trust Management, Semantic Web technology - Ontologies, Organizational Trust, Social Trust, Ontology Engineering, Conceptual Modeling, Conceptual Model, Philosophy of Social Science and Ontology of Economics, and Philosophy of economics, philosophy of social science and ontology of economics
Recently, we witnessed a shift in the Networking paradigm, with large part of the network control moving from hardware to software. This move has been accompanied by an increase of interest in declarative software models (con-ceptual... more
Recently, we witnessed a shift in the Networking paradigm, with large part of the network control moving from hardware to software. This move has been accompanied by an increase of interest in declarative software models (con-ceptual models) for the domain. Moreover, novel architectures allow services to be deployed in multiple domains. These changes call for new business models to allow the commercialization of Virtual Network Functions (VNFs). This paper proposes the creation of an ontology-based reference conceptual model to support VNF Marketplaces, allowing VNF vendors and infrastructure providers to commercialize VNFaaS (VNFs as services). The proposed reference model has been engineered by using foundational ontology techniques (UFO/OntoUML), it has been formally validated by using model simulation techniques, and it has been implemented in OWL.
Research Interests: Information Systems, Applied Ontology, Computer Networks, Ontology (Computer Science), Conceptual Modelling, and 15 moreSemantic Web technology - Ontologies, Semantic Web, Ontology Engineering, Conceptual Modeling, Computer Networking and Telecommunication, Computer Networking, Computer Network, Conceptual Model, Network Virtualization, Software defined networking, Conceptual Data Modeling, Software Defined Network, Software Defined Networks (SDN), Network Function Virtualization, and SDN NFV & Network Virtualization Industry
Over the years, there is a growing interest in employing theories from philosophical ontology, cognitive science, and linguistics to devise theoretical, methodological and computational tools for conceptual modeling, knowledge... more
Over the years, there is a growing interest in employing theories from philosophical ontology, cognitive science, and linguistics to devise theoretical, methodological and computational tools for conceptual modeling, knowledge representation, and ontology engineering. In this paper, we discuss one particular kind of such tools, namely, ontological anti-patterns. Ontological anti-patterns are error-problem modeling structures that can create a deviation between the possible and the intended interpretations of an ontology. The contributions of this paper are threefold. Firstly, we propose three empirically elicited ontological anti-patterns related to the modeling of taxonomic structures. Secondly , we advance a series of rectification plans that can be used to eliminate the occurrence of these anti-patterns in domain ontologies. Finally, we present a software tool that supports the automated detection, analysis, and elimination of these anti-patterns.
Research Interests: Information Systems, Systematics (Taxonomy), Information Science, Information Systems (Business Informatics), Ontology, and 15 moreApplied Ontology, Ontology (Computer Science), Taxonomy, Conceptual Modelling, Library and Information Science, Semantic Web technology - Ontologies, Knowledge Representation, Ontology Engineering, Conceptual Modeling, Information Science & Library Management, Conceptual Model, UML Class Diagram, Information systems models, Knowledge Representation and Reasoning, and Conceptual Data Modeling
In recent years, there has been a growing interest in modeling value in the context of Enterprise Architecture, which has been driven by a need to align the vision and strategic goals of an enterprise with its business architecture.... more
In recent years, there has been a growing interest in modeling value in the context of Enterprise Architecture, which has been driven by a need to align the vision and strategic goals of an enterprise with its business architecture. Nevertheless, the current literature shows that the concept of value is conceptually complex and still causes a lot of confusion. For example, we can find in the literature the concept of value being taken as equivalent to notions as disparate as goals, events, objects and capabilities. As a result, there is still a lack of proper support for mod-eling all aspects of value as well as its relations to these aforementioned notions. To address this issue, we propose in this paper a pattern language for value modeling in ArchiMate, which is based on the Common Ontology of Value and Risk, a well-founded reference ontology developed following the principles of the Unified Foundation Ontology. This enables us to delineate a clear ontological foundation, which addresses the ambiguous use of the value concept. The design of the Value Pattern Language will be guided by the Design Science Research Methodology. More specifically, a first iteration of the build-and-evaluate loop is presented , which includes the development of the pattern language and its demonstration by means of a case study of a low-cost airline.
Research Interests: Information Systems, Information Systems (Business Informatics), Management Information Systems, Applied Ontology, Enterprise Architecture, and 15 moreOntology (Computer Science), Conceptual Modelling, Business Information Systems, Accounting Information Systems, Value Chain, Enterprise Architecture Management, Enterprise Architecture teaching, Pattern languages, Information Systems Engineering, Business Informatics, Business Information System, Conceptual Model, Enterprise modelling, TOGAF, and Value Model
In recent years, there has been a growing interest in modeling value in the context of Enterprise Architecture, which has been driven by a need to align the vision and strategic goals of an enterprise with its business architecture.... more
In recent years, there has been a growing interest in modeling value in the context of Enterprise Architecture, which has been driven by a need to align the vision and strategic goals of an enterprise with its business architecture. Nevertheless, the current literature shows that the concept of value is conceptually complex and still causes a lot of confusion. For example, we can find in the literature the concept of value being taken as equivalent to notions as disparate as goals, events, objects and capabilities. As a result, there is still a lack of proper support for mod-eling all aspects of value as well as its relations to these aforementioned notions. To address this issue, we propose in this paper a pattern language for value modeling in ArchiMate, which is based on the Common Ontology of Value and Risk, a well-founded reference ontology developed following the principles of the Unified Foundation Ontology. This enables us to delineate a clear ontological foundation, which addresses the ambiguous use of the value concept. The design of the Value Pattern Language will be guided by the Design Science Research Methodology. More specifically, a first iteration of the build-and-evaluate loop is presented , which includes the development of the pattern language and its demonstration by means of a case study of a low-cost airline.
Research Interests:
—Risk analysis is a complex and critical activity in various contexts, ranging from strategic planning to IT systems operation. Given its complexity, several Enterprise Architecture (EA) frameworks and modeling languages have been... more
—Risk analysis is a complex and critical activity in various contexts, ranging from strategic planning to IT systems operation. Given its complexity, several Enterprise Architecture (EA) frameworks and modeling languages have been developed to help analysts in representing and analyzing risks. Yet, the notion of risk remains overloaded and conceptually unclear in most of them. In this paper, we investigate the real-world semantics underlying risk-related constructs in one of such approaches, namely ArchiMate's Risk and Security Overlay (RSO). We perform this investigation by means of ontological analysis to reveal semantic limitations in the overlay, such as ambiguity and missing constructs. Building on the results of this analysis, we propose a well-founded redesign of the risk modeling aspects of the RSO.
Research Interests: Information Systems, Risk Management and Insurance, Information Science, Information Systems (Business Informatics), Management Information Systems, and 56 moreOntology, Applied Ontology, Semantic Web Technologies, Enterprise Architecture, Organizational Theory, Information Management, Disaster risk management, Ontology (Computer Science), Project Risk Management, Risk and Vulnerability, Conceptual Modelling, Library and Information Science, Financial Risk Management, Formal Ontology, Risk Management, Business Information Systems, Risk assessment, Semantic Web technology - Ontologies, Semantic Web, Knowledge Representation, Small and Medium-scale Enterprises, Risk Assessment & Risk Management, Enterprise risk management, Supply Chain Risk Management, Conceptual Modeling, Risk Analysis, ITIL, Conceptual Modeling and Semantic Models, Enterprise Modeling, Business Information technology, Enterprise Architecture teaching, Disaster risk reduction, ITIL and IT Service Management, Business Informatics, Management Information System, Enterprise Information Systems, Business Information System, Conceptual Model, Security Risk Management, ITIL/ITSM, Risk factors, Ontologies, Knowledge representation, Semantic web, Cloud computing, Ontology based data access, Knowlede management, Knowledge Representation and Reasoning, Conceptual Data Modeling, Web Semántica, Enterprise modelling, Security and Risk Assessment, Risk and crisis management, Organizational modeling, Risk Factors, Risk Assessment, Archimate, Information Systems Development And Management, Knowledge Representations, Enterprise Security Architecture, and Governance and Risk Management
—Risk analysis is a complex and critical activity in various contexts, ranging from strategic planning to IT systems operation. Given its complexity, several Enterprise Architecture (EA) frameworks and modeling languages have been... more
—Risk analysis is a complex and critical activity in various contexts, ranging from strategic planning to IT systems operation. Given its complexity, several Enterprise Architecture (EA) frameworks and modeling languages have been developed to help analysts in representing and analyzing risks. Yet, the notion of risk remains overloaded and conceptually unclear in most of them. In this paper, we investigate the real-world semantics underlying risk-related constructs in one of such approaches, namely ArchiMate's Risk and Security Overlay (RSO). We perform this investigation by means of ontological analysis to reveal semantic limitations in the overlay, such as ambiguity and missing constructs. Building on the results of this analysis, we propose a well-founded redesign of the risk modeling aspects of the RSO.
Research Interests:
In this paper we review and discuss some recent attempts at ontological re-engineering of REA in the light of the UFO ontology and the OntoUML language, focusing in particular on different choices concerning the UFO notion of relator. We... more
In this paper we review and discuss some recent attempts at ontological re-engineering of REA in the light of the UFO ontology and the OntoUML language, focusing in particular on different choices concerning the UFO notion of relator. We also take this as an opportunity to clarify and revise Guarino and Guizzardi's general theory of reification and truthmaking proposed in the past.
Research Interests:
It is widely recognized that accurately identifying competitors is a challenge for many companies and entrepreneurs. It is one that they cannot escape from, as failing to do so is a recipe for problems. Amongst other factors, competitor... more
It is widely recognized that accurately identifying competitors is a challenge for many companies and entrepreneurs. It is one that they cannot escape from, as failing to do so is a recipe for problems. Amongst other factors, competitor identification is challenging because of the complex nature of the competitive relationships that arise in business environments. In this paper, we tackle this issue by means of an initial ontological analysis on competition grounded in the Unified Foun-dational Ontology (UFO). Our analysis, the first of its kind in the literature , allows us to explain why and when competition arises, as well as to distinguish between different three types of competitive relationships, namely direct, indirect and potential competition.
Research Interests:
The construction of large-scale reference conceptual models is a com- plex engineering activity. To develop high-quality models, a modeler must have the support of expressive engineering tools such as theoretically well-founded modeling... more
The construction of large-scale reference conceptual models is a com- plex engineering activity. To develop high-quality models, a modeler must have the support of expressive engineering tools such as theoretically well-founded modeling languages and methodologies, patterns and anti-patterns and automated support environments. This paper proposes Semantic Anti-Patterns for ontology- driven conceptual modeling. These anti-patterns capture error prone modeling decisions that can result in the creation of models that allow for unintended mod- el instances (representing undesired state of affairs). The anti-patterns presented here have been empirically elicited through an approach of conceptual models validation via visual simulation. The paper also presents a tool that is able to: au- tomatically identify these anti-patterns in user’s models, provide visualization for its consequences, and generate corrections to these models by the automatic in- clusion of OCL constraints.
Research Interests: Information Systems, Information Science, Management Information Systems, Applied Ontology, Semantic Web Technologies, and 24 moreOntology (Computer Science), Conceptual Modelling, Library and Information Science, Design Patterns, Semantic Web technology - Ontologies, Semantic Web, Conceptual Modeling, Medical Semantics and Ontology, Software design, patterns, conceptual modelling, Information Systems Analysis and Design, Conceptual Modeling and Semantic Models, OOAD, UML, Design Patterns, semantic interoperbility, ontology, serious games, e-learning, computer architectures, computer modelling and behavioural simulation, Information Systems Engineering, Library & Information Science, Management Information System, Library and Information Sciences, Design Pattern, Conceptual Models, Information systems analysis and design methods, Conceptual Data Modeling, Software Design Patterns, Ontological Conceptual modeling, OntoUML, and Conceptual Modeling and Simulations
Given the increasing complexity of ontology-driven conceptual modeling and on-tology engineering, there is an urging need for developing a new generation of complexity management tools for these disciplines [12]. These include a number of... more
Given the increasing complexity of ontology-driven conceptual modeling and on-tology engineering, there is an urging need for developing a new generation of complexity management tools for these disciplines [12]. These include a number of methodological and computational tools that are grounded on sound ontological foundations. In particular, we should advance in these disciplines a well-tested body of knowledge in terms of Ontology Patterns, Ontology Pattern Languages and Ontological Anti-Patterns. This chapter focuses on the latter. An anti-pattern is a recurrent error-prone modeling decision [15]. In this paper , we are interested in one specific sort of anti-patterns, namely, model structures that, albeit producing syntactically valid conceptual models, are prone to result in unintended domain representations. In other words, we are interested in configurations that when used in a model will typically cause the set of valid (possible) instances of that model to differ from the set of instances representing intended state of affairs in that domain [11]. We name these configurations Anti-Patterns for Ontology-Driven Conceptual Modeling, or simply, Ontological Anti-Patterns. In this chapter, we focus on the study of Ontological Anti-Patterns in a particular conceptual modeling language named OntoUML [11]. OntoUML is an
Research Interests: Applied Ontology, Ontology (Computer Science), Conceptual Modelling, Design Patterns, Semantic Web technology - Ontologies, and 9 moreSemantic Web, Knowledge Representation, Conceptual Modeling, Software design, patterns, conceptual modelling, Conceptual Modeling and Semantic Models, Conceptual Model, Design Pattern, Anti-patterns, and Conceptual Data Modeling
—In competitive markets, companies need well-designed business strategies if they seek to grow and obtain sustainable competitive advantage. At the core of a successful business strategy there is a carefully crafted value proposition,... more
—In competitive markets, companies need well-designed business strategies if they seek to grow and obtain sustainable competitive advantage. At the core of a successful business strategy there is a carefully crafted value proposition, which ultimately defines what a company delivers to its customers. Despite their widely recognized importance, there is however little agreement on what exactly value propositions are. This lack of conceptual clarity harms the communication among stakeholders and the harmonization of current business strategy theories and strategy support frameworks. Furthermore , it hinders the development of systematic methodologies for crafting value propositions, as well as adequate support for representing and analyzing them. In this paper, we present an ontological analysis of value propositions based on a review of most relevant business and marketing theories and on previous work on value ascription, grounded in the Unified Foundational Ontology (UFO). Our investigation clarifies how value propositions are different from value presentations, and shows the difference between value propositions at the business level from those related to specific offerings.
Research Interests: Information Systems (Business Informatics), Economics, Microeconomics, Ontology, Philosophy of Economics (Philosophy), and 67 moreApplied Ontology, Organizational Theory, Business Modeling, Business Process Management, Ontology (Computer Science), Conceptual Modelling, Philosophy Of Economics, Value Theory, Business Process Modeling, Organizational Design & Engineering, Social Ontology, Business Information Systems, Semantic Web technology - Ontologies, Accounting Information Systems, Strategy (Business), Semantic Data Base, Philosophy of economics (Economics), Business Strategies, Conceptual Modeling, Software design, patterns, conceptual modelling, Ontologies, Conceptual Modeling and Semantic Models, Enterprise Modeling, Enterprise Engineering, Business Models, Business Information technology, business Strategic planning, balanced scorecard, performace measurement, risk management and organisational cost management and control, Business Model Innovation, Value Management, Value Creation, Business Informatics, Accounting and Information Systems, Business Strategy, Enterprise Information Systems, Business Model, Business Information System, Accounting Information System, Conceptual Model, Philosophy and Economics, Enterprise Modelling, UML, Ontologies, Enterprise Computing, Business Process Modelling, Philosophy of Economics and Accounting, Ontologies, Knowledge representation, Semantic web, Cloud computing, Ontology based data access, Knowlede management, Domain Ontologies, Dept of Accounting and Information System, Business Plans & Marketing Strategy, Philosophy and Economy, Ontologies and Web semantics, Businesss Strategies, Database conceptual and logical design; Data modeling, Value Propositions, Conceptual Data Modeling, Business-level strategy, Enterprise modelling, Philosophy & Economics, Organizational modeling, Economics and philosophy, Business Model Canvas, Database Modelling and Semantics, Value Co-creation and Business Model Innovation, Enteprise Architecture, Philosophy of Social Science and Ontology of Economics, Value Model, Value Proposition Canvas, Dept of Accounting and Information System Education Social Sciences Philosophy Business, and Accounting and Information System
Over the years, there has been an increasing adoption of ontology-driven conceptual models to represent the conceptual structure of critical domains in reality. Given the complexity of this task, there has been a growing demand for the... more
Over the years, there has been an increasing adoption of ontology-driven conceptual models to represent the conceptual structure of critical domains in reality. Given the complexity of this task, there has been a growing demand for the development of proper engineering tools for supporting the design of these models. Despite a number of advances in this area, there is still a shortage of tools directed at novice and non-technical users that can, at the same time, address two competing requirements, namely: maintain modeling expressivity by being able to represent true ontological distinctions, while remaining intuitive and easy to learn by this class of users. In this article, we sketch a proposal in this direction by introducing the idea of an Ontology Model Canvas.
Research Interests: Information Systems, Information Science, Information Systems (Business Informatics), Ontology, Library Science, and 34 moreBusiness Modeling, Ontology (Computer Science), Ontology Development, Conceptual Modelling, Library and Information Science, Business Information Systems, Semantic Web technology - Ontologies, Semantic Web, Strategy (Business), Knowledge Representation, Ontology Engineering, Conceptual Modeling, Ontologies, Ontología, Conceptual Modeling and Semantic Models, Business Models, Ontologias, Business Informatics, enterprise Ontology, Business Strategy, Library & Information Science, Information Science & Library Management, Business Model, Ontologia, Business Information System, Conceptual Model, Ontology Design, Ontologies, Knowledge representation, Semantic web, Cloud computing, Ontology based data access, Knowlede management, Knowledge Representation and Reasoning, Conceptual Data Modeling, Web Semántica, Ontologías, Ontology Knowledge Engineering, and Business Model Canvas
—In competitive markets, companies need well-designed business strategies if they seek to grow and obtain sustainable competitive advantage. At the core of a successful business strategy there is a carefully crafted value proposition,... more
—In competitive markets, companies need well-designed business strategies if they seek to grow and obtain sustainable competitive advantage. At the core of a successful business strategy there is a carefully crafted value proposition, which ultimately defines what a company delivers to its customers. Despite their widely recognized importance, there is however little agreement on what exactly value propositions are. This lack of conceptual clarity harms the communication among stakeholders and the harmonization of current business strategy theories and strategy support frameworks. Furthermore , it hinders the development of systematic methodologies for crafting value propositions, as well as adequate support for representing and analyzing them. In this paper, we present an ontological analysis of value propositions based on a review of most relevant business and marketing theories and on previous work on value ascription, grounded in the Unified Foundational Ontology (UFO). Our investigation clarifies how value propositions are different from value presentations, and shows the difference between value propositions at the business level from those related to specific offerings.
Research Interests:
Enterprise information systems are increasingly being conceived as a combination of existing systems and to work as a part of an ecosystem of software products. This change demands methods and tools to deal with the challenging semantic... more
Enterprise information systems are increasingly being conceived as a combination of existing systems and to work as a part of an ecosystem of software products. This change demands methods and tools to deal with the challenging semantic interoperability issues. OntoUML is a well-founded modeling language that allows modelers to formalize world-views in a technologically neutral way, aiding in the solution of such interoperability challenges. In this paper, we present an overview of the OntoUML Lightweight Editor (OLED), our model-based environment to build, evaluate and implement OntoUML models, alongside with its main features and application scenarios.
Research Interests: Information Systems, Applied Ontology, Ontology (Computer Science), Ontology Development, Conceptual Modelling, and 6 moreSemantic Web technology - Ontologies, Knowledge Representation, Ontology Engineering, OOAD, UML, Design Patterns, semantic interoperbility, ontology, serious games, e-learning, computer architectures, computer modelling and behavioural simulation, Ontologies, Knowledge representation, Semantic web, Cloud computing, Ontology based data access, Knowlede management, and Knowledge Representation and Reasoning
The preparation of high quality emergency plans to guide operational decisions is an approach to mitigate the emergency management complexity. In such multidisciplinary scenario, teams with different perspectives need to collaborate... more
The preparation of high quality emergency plans to guide operational decisions is an approach to mitigate the emergency management complexity. In such multidisciplinary scenario, teams with different perspectives need to collaborate towards a common goal and interact within a common understanding. In this scenario, the characterization of the variability of the elements involved in these plans is an important issue, which is addressed by the emergency plans generation methodology Document Product Line for Emergency Plans (DPL(EP)). To increase common understanding of plans, we propose an adaptation of this methodology by applying a well-founded emergency ontology, termed OntoEmergePlan, which supports the domain engineering phase. It is grounded in a foundational ontology, which ensures a higher consistency degree to the process of plans generation.
Research Interests: Semantic Web Technologies, Ontology (Computer Science), Conceptual Modelling, Semantic Web technology - Ontologies, Semantic Web, and 6 moreConceptual Modeling, Conceptual Modeling and Semantic Models, Conceptual Models, Conceptual Data Modeling, Ontological Conceptual modeling, and Conceptual Modeling and Simulations
The construction of large-scale reference conceptual models is a complex engineering activity. To develop high-quality models, a modeler must have the support of expressive engineering tools such as theoretically well-founded modeling... more
The construction of large-scale reference conceptual models is a complex engineering activity. To develop high-quality models, a modeler must have the support of expressive engineering tools such as theoretically well-founded modeling languages and methodologies, patterns and anti-patterns and automated supporting environments. This paper proposes a set of Ontological Anti-Patterns for Ontology-Driven Conceptual Modeling. These anti-patterns capture error-prone modeling decisions that can result in the creation of models that fail to exclude unintended model instances (representing unintended state of affairs) or forbid intended ones (representing intended states of affairs). The anti-patterns presented here have been empirically elicited through an approach of conceptual models validation via visual simulation. The paper also presents a series of refactoring plans for rectifying the models in which these anti-patterns occur. In addition, we present here a computational tool that is able to: automatically identify these anti-patterns in user’s models, guide users in assessing their consequences, and generate corrections to these models by the automatic inclusion of OCL constraints implementing the proposed refactoring plans. Finally, the paper also presents an empirical study for assessing the harmfulness of each of the uncovered anti-patterns (i.e., the likelihood that its occurrence in a model entails unintended consequences) as well as the effectiveness of the proposed refactoring plans.
Research Interests: Information Systems, Information Systems (Business Informatics), Applied Ontology, Semantic Web Technologies, Ontology (Computer Science), and 13 moreOntology Development, Conceptual Modelling, Design Patterns, Semantic Web technology - Ontologies, Semantic Web, Knowledge Representation, Ontology Engineering, Conceptual Modeling, UML, OOAD, UML, Design Patterns, semantic interoperbility, ontology, serious games, e-learning, computer architectures, computer modelling and behavioural simulation, UML Class Diagram, Ontologies, Knowledge representation, Semantic web, Cloud computing, Ontology based data access, Knowlede management, and Knowledge Representation and Reasoning
Ontology-driven conceptual modeling is the activity of capturing and formalizing how a community perceives a domain of interest, using modeling primitives inherited from a foundational ontology. OntoUML is an example of a language that... more
Ontology-driven conceptual modeling is the activity of capturing and formalizing how a community perceives a domain of interest, using modeling primitives inherited from a foundational ontology. OntoUML is an example of a language that supports such activity, whose design derives from the Unified Foundational Ontology (UFO).
Ontologies, in the sense of reference conceptual models, are useful in many fields. They include model-driven development of software systems, development of knowledge-based application (in the context of Semantic Web), semantic interoperability between information systems, and evaluation of modeling languages, to cite some. Regardless of the application, the quality of an ontology is directly related the quality of the results.
Ontology and conceptual model quality encompasses a vast range of criteria. The validation activity aims to improve the domain appropriateness of a model. This means to help improve modeler’s confidence in saying: “I built the right model for my domain”.
This thesis presents a validation framework usable by “managers” of the ontology world, i.e. modelers that are not experts in validation, logics and formal methods. The framework contains techniques and tools to help modelers systematically improve the quality of their models without demanding costly learning requirements. We build our framework on two conceptual pillars: model simulation and anti-patterns.
Ontologies, in the sense of reference conceptual models, are useful in many fields. They include model-driven development of software systems, development of knowledge-based application (in the context of Semantic Web), semantic interoperability between information systems, and evaluation of modeling languages, to cite some. Regardless of the application, the quality of an ontology is directly related the quality of the results.
Ontology and conceptual model quality encompasses a vast range of criteria. The validation activity aims to improve the domain appropriateness of a model. This means to help improve modeler’s confidence in saying: “I built the right model for my domain”.
This thesis presents a validation framework usable by “managers” of the ontology world, i.e. modelers that are not experts in validation, logics and formal methods. The framework contains techniques and tools to help modelers systematically improve the quality of their models without demanding costly learning requirements. We build our framework on two conceptual pillars: model simulation and anti-patterns.
Research Interests:
The usage of graphic models to formalize and structure knowledge of a specific domain is not recent. In the last few years, ontologies have been used to achieve this goal in areas such as software engineering and open data government.... more
The usage of graphic models to formalize and structure knowledge of a specific domain is not recent. In the last few years, ontologies have been used to achieve this goal in areas such as software engineering and open data government. Regardless of the language used to describe these models, the difficulty of assessing their quality is widely known. In recent researches, the Alloy language has been used as a way to evaluate graphic models, aiding the professionals that build them. OntoUML is a well-founded language to build ontologies. The existence of algorithms that translate models developed in this language to Alloy specifications has also motivated this research. In this sense, it was needed to assess the actual improvement in models that this approach provides. Nonetheless, with the results of the validation in hand, this work identifies recurrent modeling decisions that are error prone. Since OntoUML is a lightweight extension of UML, most of these patterns are also suitable ...
To cope with increasingly dynamic and competitive markets, enterprises need carefully formulated strategies in order to improve their processes, develop sustainable business models and offer more attractive products and services to their... more
To cope with increasingly dynamic and competitive markets, enterprises need carefully formulated strategies in order to improve their processes, develop sustainable business models and offer more attractive products and services to their customers. To help them make sense of this complex environment, enterprises resort to an array of strategic business analysis tools and techniques, such as SWOT and the Business Model Canvas. Most of the tools, however, are derived from informally defined social and economic concepts, which hinders their reuse by practitioners. In this thesis, we address this limitation by means of in-depth ontological analyses conducted under the principles of the Unified Foundational Ontology (UFO). In particular, we focus on the notions of value, risk and competition, as these are recurrently employed by many techniques and yet, still suffer conceptual and definitional shortcomings. One main contribution of this thesis is the Common Ontology of Value and Risk (COVER), a reference conceptual model that disentangle and clarifies several perspectives on value and risk, while demonstrating that they are ultimately two ends of the same spectrum. We demonstrate the usability and relevance of COVER by means of two applications in ArchiMate, an international standard for enterprise architecture representation. A second contribution is the Ontology of Competition, which formally characterizes competitive relationships and defines the nature of several competitive relationships arising in business markets.