On the Mathematics of Modelling, Metamodelling, Ontologies and Modelling Languages [electronic resource] /
Henderson-Sellers, Brian.author.
Computing as a discipline is maturing rapidly. However, with maturity often comes a plethora of subdisciplines, which, as time progresses, can become isolationist. The subdisciplines of modelling, metamodelling, ontologies and modelling languages within software engineering e.g. have, to some degree, evolved separately and without any underpinning formalisms. Introducing set theory as a consistent underlying formalism, Brian Henderson-Sellers shows how a coherent framework can be developed that clearly links these four, previously separate, areas of software engineering. In particular, he shows how the incorporation of a foundational ontology can be beneficial in resolving a number of controversial issues in conceptual modelling, especially with regard to the perceived differences between linguistic metamodelling and ontological metamodelling. An explicit consideration of domain-specific modelling languages is also included in his mathematical analysis of models, metamodels, ontologies and modelling languages. This encompassing and detailed presentation of the state-of-the-art in modelling approaches mainly aims at researchers in academia and industry. They will find the principled discussion of the various subdisciplines extremely useful, and they may exploit the unifying approach as a starting point for future research.
Description based upon print version of record.
On the Mathematics of Modelling, Metamodelling,Ontologies andModelling Languages; Preface; Contents; Abstract; 1 Introduction; 2 Mathematics for Modelling; 2.1...Morphisms; 2.2...Functions; 2.3...Relationships; 2.3.1 Definition; 2.3.2 Abstraction Relationship; 2.3.3 Representation and Interpretation Relationships; 2.3.4 Classification and Generalization; 2.3.5 Instantiation; 2.3.6 Relating Classification, Generalization and Instantiation; 2.3.7 Conformance; 3 Models; 3.1...Token and Type Models; 3.2...Descriptive or Prescriptive?; 3.3...Interpretive Mappings; 3.4...Theories and Layered Architectures
3.5...Types of Models: Summary4 Metamodels; 5 Ontologies; 5.1...Linguistic and Ontological Metamodelling; 6 Modelling Languages; 7 Linking Models, Metamodels, Ontologies and Modelling Languages; 7.1...Metamodels and Modelling Languages; 7.1.1 First School of Thought; 7.1.2 Second School of Thought; 7.1.3 Reconciliation of Two Schools of Thought; 7.2...Augmentation by the Introduction of Ontologies; 7.3...Incorporating DSMLs; 8 Other Related Work and Further Discussion; 9 Conclusions and Further Work; References
Includes bibliographical references.
Computing as a discipline is maturing rapidly. However, with maturity often comes a plethora of subdisciplines, which, as time progresses, can become isolationist. The subdisciplines of modelling, metamodelling, ontologies and modelling languages within software engineering e.g. have, to some degree, evolved separately and without any underpinning formalisms. Introducing set theory as a consistent underlying formalism, Brian Henderson-Sellers shows how a coherent framework can be developed that clearly links these four, previously separate, areas of software engineering. In particular, he shows how the incorporation of a foundational ontology can be beneficial in resolving a number of controversial issues in conceptual modelling, especially with regard to the perceived differences between linguistic metamodelling and ontological metamodelling. An explicit consideration of domain-specific modelling languages is also included in his mathematical analysis of models, metamodels, ontologies and modelling languages. This encompassing and detailed presentation of the state-of-the-art in modelling approaches mainly aims at researchers in academia and industry. They will find the principled discussion of the various subdisciplines extremely useful, and they may exploit the unifying approach as a starting point for future research.
Software engineering.
Computers.
Mathematical logic.
Artificial intelligence.
Software Engineering.
Models and Principles.
Computation by Abstract Devices.
Mathematical Logic and Formal Languages.
Artificial Intelligence.
SpringerBriefs in Computer Science