On the symbiosis between conceptual modeling and ontology engineering : recommendation-based conceptual modeling

Within an enterprise, different conceptual models, such as process, data, and goal models, are created by various stakeholders. These models are fundamentally based on similar underlying enterprise (domain) concepts, but they have a different focus, are represented using different modeling languages, take different viewpoints, utilize different terminology, and are used to develop different enterprise artefacts (such as documents, software, databases, etc.); therefore, they typically lack consistency and alignment. Another issue is that modelers have different vocabulary selections and different modeling styles. As a result, the enterprise can find itself accumulating a pile of models which cover similar aspects in different manners. Those models are not machine-readable and cannot be processed automatically. Enterprise-Specific Ontologies (ESOs) aim to solve this problem by serving as a reference during the conceptual model creation. Using such a shared semantic repository makes conceptual models semantically aligned and facilitates model integration. However, managing those ontologies is complicated; an enterprise is an evolving entity, and as it changes, the ESO might become outdated. During the years of research dedicated to this dissertation, the Recommendation-Based Conceptual Modeling and Ontology Evolution (CMOE+) framework was developed. This framework establishes a symbiotic relationship between the Ontology engineering and the Conceptual modeling fields. CMOE+ consists of two cycles: the Ontology Evolution cycle and the Conceptual Modeling cycle. The Ontology Evolution cycle is responsible for setting up the initial version of the ESO and updating it as the knowledge within the enterprise evolves. Additionally, this cycle encapsulates recommendation services to perform ontology look-up and to present the most relevant ESO concepts in support of the modeler. The Conceptual Modeling cycle is responsible for the creation of conceptual models in different modeling languages based on the ESO. This cycle is also concerned with the quality evaluation of the created models. CMOE+ was developed based on requirements identified as a result of a literature review and a case study. The development process follows the Design Science Research Methodology (DSRM). After the initial version of CMOE+ was put forward, our focus was narrowed towards the recommendation-based conceptual modeling part of CMOE+, and we continued to gradually improve the framework in iterations until it reached its current state. The Ontology Evolution Cycle is not fully addressed within the scope of this dissertation. In order to demonstrate the performance and usability of CMOE+, it was exemplified for process modeling using BPMN and goal modeling using i*. This thesis presents a detailed instantiation, and explains steps to be performed in order to instantiate CMOE+ for other modeling languages. In order to evaluate the process modeling instance of CMOE+, a CMOE+BPMN tool was implemented. This tool incorporates a BPMN modeler, facilitates storage and access of the ESO, and includes all algorithms functioning within CMOE+ for the BPMN modeling language (as some of the algorithms are language dependent). Next, CMOE+ was exemplified using the i* goal modeling language. Finally, we demonstrated the ability of CMOE+ to perform alignment between i* and BPMN models, in order to show that CMOE+ is indeed beneficial in achieving interoperability among models created in different modeling languages and covering distinct aspects of the enterprise

Enterprise-specific ontology-driven process modelling

Different process models are created within an enterprise by different modelers who use different enterprise terms. This hinders model interoperability and integration. A possible solution is formalizing the vocabulary used within the enterprise in an ontology and put this ontology as bases for constructing process models. Given that an enterprise is an evolving entity, the ontology needs to evolve to properly reflect the domain of the enterprise. This paper proposes an enterprise-specific ontology-driven process modelling method which tackles the two aforementioned issues by assisting the modeller in creating process models using terminology from the ontology and simultaneously supporting ontology enrichment with feedback from those models. When the modeller creates a model, matching mechanisms incorporated in the method are working together to suggest a list of ontological concepts that have a high potential to be useful for a particular modelling element. When the model is created, its quality is first evaluated from different perspectives to make sure that it can be used within the enterprise, and second to discover whether its feedback can be useful for the ontology. When the feedback is extracted, the proposed method incorporates guidelines on how to use this feedback

Recommendation-Based Conceptual Modeling and Ontology Evolution Framework (CMOE+)

Within an enterprise, various stakeholders create different conceptual models, such as process, data, and requirements models. These models are fundamentally based on similar underlying enterprise (domain) concepts, but they differ in focus, use different modeling languages, take different viewpoints, utilize different terminology, and are used to develop different enterprise artifacts; as such, they typically lack consistency and interoperability. This issue can be solved by enterprise-specific ontologies, which serve as a reference during the conceptual model creation. Using such a shared semantic repository makes conceptual models interoperable and facilitates model integration. The challenge to accomplish this is twofold: on the one hand, an up-to-date enterprise-specific ontology needs to be created and maintained, and on the other hand, different modelers also need to be supported in their use of the enterprise-specific ontology. The authors propose to tackle these challenges by means of a recommendation-based conceptual modeling and an ontology evolution framework, and we focus in particular on ontology-based modeling support. To this end, the authors present a framework for Business Process Modeling Notation (BPMN) as a conceptual modeling language, and focus on how modelers can be assisted during the modeling process and how this impacts the semantic quality of the resulting models. Subsequently, a first, large-scale explorative experiment is presented involving 140 business students to evaluate the BPMN instantiation of our framework. The experiments show promising results with regard to incurred overheads, intention of use and model interoperability

Comparing XML Files with a DOGMA Ontology to Generate Ω-RIDL Annotations

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Supporting process model development with enterprise-specific ontologies

Within an enterprise, different models – even of the same type - are typically created by different modellers. Those models use different terminology, are based on different semantics and are thus hard to integrate. A possible solution is using an enterprise-specific ontology as a reference during model creation. This allows basing all the models created within one enterprise upon a shared semantic repository, mitigating the need for model integration and promoting interoperability. The challenge here is that the enterprise-specific ontology can be very extensive, making it hard for the modeller to select the appropriate ontology concepts to associate with model elements. In this paper we focus on process modelling, and develop a method that uses four different matching mechanisms to suggest the most relevant enterprise-specific ontology concepts to the modeller while he is creating the model. The first two utilize string and semantic matching techniques (i.e., synonyms) to compare the BPMN construct’s label with enterprise-specific ontology concepts. The other two exploit the formally defined grounding of the enterprise ontology in a core ontology to make suggestions, based on the BPMN construct type and the relative position (in the model). We show how our method leads to semantically annotated process models, and demonstrate it using an ontology in the financial domain