Ontology-driven conceptual modeling: A'systematic literature mapping and review

All rights reserved. Ontology-driven conceptual modeling (ODCM) is still a relatively new research domain in the field of information systems and there is still much discussion on how the research in ODCM should be performed and what the focus of this research should be. Therefore, this article aims to critically survey the existing literature in order to assess the kind of research that has been performed over the years, analyze the nature of the research contributions and establish its current state of the art by positioning, evaluating and interpreting relevant research to date that is related to ODCM. To understand and identify any gaps and research opportunities, our literature study is composed of both a systematic mapping study and a systematic review study. The mapping study aims at structuring and classifying the area that is being investigated in order to give a general overview of the research that has been performed in the field. A review study on the other hand is a more thorough and rigorous inquiry and provides recommendations based on the strength of the found evidence. Our results indicate that there are several research gaps that should be addressed and we further composed several research opportunities that are possible areas for future research

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

Conceptual modelling and the quality of ontologies: A comparison between object-role modelling and the object paradigm

Ontologies are key enablers for sharing precise and machine-understandable semantics among different applications and parties. Yet, for ontologies to meet these expectations, their quality must be of a good standard. The quality of an ontology is strongly based on the design method employed. This paper addresses the design problems related to the modelling of ontologies, with specific concentration on the issues related to the quality of the conceptualisations produced. The paper aims to demonstrate the impact of the modelling paradigm adopted on the quality of ontological models and, consequently, the potential impact that such a decision can have in relation to the development of software applications. To this aim, an ontology that is conceptualised based on the Object Role Modelling (ORM) approach is re-engineered into a one modelled on the basis of the Object Paradigm (OP). Next, the two ontologies are analytically compared using the specified criteria. The conducted comparison highlights that using the OP for ontology conceptualisation can provide more expressive, reusable, objective and temporal ontologies than those conceptualised on the basis of the ORM approach

OntoEng: A design method for ontology engineering in information systems

This paper addresses the design problem relating to ontology engineering in the discipline of information systems. Ontology engineering is a realm that covers issues related to ontology development and use throughout its life span. Nowadays, ontology as a new innovation promises to improve the design, semantic integration, and utilization of information systems. Ontologies are the backbone of knowledge-based systems. In addition, they establish sharable and reusable common understanding of specific domains amongst people, information systems, and software agents. Notwithstanding, the ontology engineering literature does not provide adequate guidance on how to build, evaluate, and maintain ontologies. On the basis of the gathered experience during the development of V4 Telecoms Business Model Ontology as well as the conducted integration of the related literature from the design science paradigm, this paper introduces OntoEng and its application as a novel systematic design method for ontology engineering

Conceptual Modelling and The Quality of Ontologies: Endurantism Vs. Perdurantism

Ontologies are key enablers for sharing precise and machine-understandable semantics among different applications and parties. Yet, for ontologies to meet these expectations, their quality must be of a good standard. The quality of an ontology is strongly based on the design method employed. This paper addresses the design problems related to the modelling of ontologies, with specific concentration on the issues related to the quality of the conceptualisations produced. The paper aims to demonstrate the impact of the modelling paradigm adopted on the quality of ontological models and, consequently, the potential impact that such a decision can have in relation to the development of software applications. To this aim, an ontology that is conceptualised based on the Object-Role Modelling (ORM) approach (a representative of endurantism) is re-engineered into a one modelled on the basis of the Object Paradigm (OP) (a representative of perdurantism). Next, the two ontologies are analytically compared using the specified criteria. The conducted comparison highlights that using the OP for ontology conceptualisation can provide more expressive, reusable, objective and temporal ontologies than those conceptualised on the basis of the ORM approach

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

On the role of domain ontologies in the design of domain-specific visual modeling langages

Domain-Specific Visual Modeling Languages should provide notations and abstractions that suitably support problem solving in well-defined application domains. From their user’s perspective, the language’s modeling primitives must be intuitive and expressive enough in capturing all intended aspects of domain conceptualizations. Over the years formal and explicit representations of domain conceptualizations have been developed as domain ontologies. In this paper, we show how the design of these languages can benefit from conceptual tools developed by the ontology engineering community

Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

Enterprise model verification and validation : an approach

This article presents a verification and validation approach which is used here in order to complete the classical tool box the industrial user may utilize in enterprise modeling and integration domain. This approach, which has been defined independently from any application domain is based on several formal concepts and tools presented in this paper. These concepts are property concepts, property reference matrix, properties graphs, enterprise modeling domain ontology, conceptual graphs and formal reasoning mechanisms