Modeling Basis

2.2.1 Semiotic triangle

Modeling at the conceptual level is an activity of representing phenomena of the real world in a model. It complies with the famous semiotic triangle adapted from Ogden and Richards’ triangle of meaning [121], i.e. the relationships between a concept, a referent and a sign. Concepts are mental things, words of mind. A referent is a thing in reality to which a concept refers. Signs are expressions, symbols or labels to signify concepts in some language. Modeling aims to conceptualize referents by employing modeling signs in certain languages. Obviously models could be quite different from each other due to what (referent) to model, how to model (concept) and with what (sign) to model. The three elements interact on each other from a semantic representation perspective. The capability of signs’ expressiveness can determine what semantics of referents can be represented, also how semantics are conceptualized.

2.2.MODELINGBASIS13

Figure 2.1: Zachman Enterprise Architecture Framework [189] [171] [214]

14 CHAPTER 2. PROBLEM SETTING 2.2.2 Modeling language, meta-model and model semantics

Any model must be built in a certain modeling language. A modeling language is any artificial language that can be used to express information or knowledge or systems in a structure that is defined by a consistent set of rules. A modeling language is used to represents concepts and relationships and other phenomena with a set of graphical notations or symbols. Such a modeling language is often called graphical modeling language. ER [15], UML [125], BPMN [12], EPC [160] are examples of graphical modeling languages. A modeling language can also be a set of standardized textual keywords or markup language accompanied by parameters, for instance, OWL [195], BPML [11], BPEL4WS [116], and EPML [105]. Graphical modeling languages usu-ally facilitate readability of models whilst textual modeling languages enable models machine-interpretable and tool-interchangeable.

Interpretations of the meaning of modeling components in a modeling language are generally defined in a meta-model. A modeling components such as a graphical notation or a symbol and a textual keyword or a markup label is called a meta-model element or a modeling construct in our work. Meta-model elements defined in a meta-model are the building bricks of a model. A meta-model is also a model of a domain of interest and it is an instance of a meta-meta-model. A model in a certain modeling language is the instance of the meta-model of this modeling language. A model having all the instances of user objects is the instance of model. Instantiated model, model, meta-model and meta-meta-meta-model respectively correspond to the M0, M1, M2 and M3 layers of OMG’s four layer meta-data architectures [124].

In this thesis, meta-meta-model is beyond our research focus. While, instantiated model at the M0 layer is too specific to be reused. We therefore mainly focus on M1 and M2 layers, i.e. model and meta-model.

Common uses of meta-models are as follows [207]:

• As a schema for semantic data that needs to be exchanged or stored.

• As a language that supports a particular method or process.

• As a language to express additional semantics of existing information.

The three usages of meta-models are all employed in the thesis. The meta-model of a business process model supports process modeling. It is also stored as a schema associated with a model file. Facilitating the exchange of different process models is one of our research goals so that meta-model is one of our research objects. Establishing a semantic annotation method itself is a process of creating a meta-model to specify the additional semantics of an existing model.

The term semantics in linguistics means the study or science of meaning in language, or the study of relationships between signs and symbols and what they represent. It also indicates the meaning or the interpretation of a word, sentence, or other language form [3]. Model semantics is hereby the meaning or the interpretation of a model. Model semantics are represented by model elements. Model elements are the components of a model. Interpretation of model elements usually depends on the context of the system which the models represent the solutions for. Besides, model elements are composed of modeling constructs, and the semantics of modeling constructs are defined in a

2.2. MODELING BASIS 15 meta-model of the modeling language. Thus, we conclude that model semantics are interpreted according to the system’s context and the modeling language applied in the solution.

2.2.3 Ontology-driven and domain-specific

Modeling at the conceptual level trends to be seemingly independent of the computer world in the development of modeling methodologies. Researchers turn to philosophy and linguistics to try to find the inherent principles in the concepts of real world do-mains. And the boundary between the conceptual modeling and knowledge modeling becomes gradually fuzzy.

For example, Ontology-Driven Conceptual Modeling draws fundamental notions from formal ontology and establishes a minimal top-level ontology to drive concep-tual modeling [47]. Thesaurus Concepconcep-tual Model uses the Knowledge/Data Model (KDM) [137] that incorporates an object-oriented view of data, together with knowl-edge regarding its usage [68]. Ontologies are used for the development of conceptual schemas of information systems by pruning irrelevant concepts in the ontologies [18].

On the other hand, domain model framework and meta-model make reuse and flexibility of models possible. A software system can never be finished, new and changed domain concepts will always be appearing, forcing continuous rebuilding, testing and re-deployment of systems [8]. The main idea of the approach proposed in [8] is the removal of domain concepts from concrete software and database models, into standardized vocabularies and libraries of domain concept models. Re-engineering software and database is done using a generic reference object model (ROM) system architecture in [8]. Reference models are used in [160] to combine "formal driven" and "content driven" approaches in a new way to develop information systems. The formal driven approach aims to develop and implementing a technical correct running system. The goal of the content driven approach is developing and implementing an organizational correct running system. Goals of content and technology are concerned in reference models, which are regarded as "blue prints" for business engineering and can be used to model and optimize business processes. Domain-specific methods implemented with metaCASE technology [67] use modeling languages to develop a domain meta-model mapped to combinations of components in order to generate a product.

All these approaches address the process of modeling, and the ideas can be ana-logically applied on the results of modeling — models. In an ontology-based semantic annotation method, the utility of an ontology and domain knowledge is applied af-ter modeling. The additional semantic treatment is still necessary for the exchange or reuse of models across different organizations, because a modeling process usually is not centralized and local ontologies and local domain/reference models applied in the modeling are still various from different organizations. In this work, consensual ontologies and domain reference models are therefore needed to be determined in an annotation phase based on the requirements on interoperability and applications.

16 CHAPTER 2. PROBLEM SETTING