Ontological Evaluation of Conceptual Models

The objective of this paper is to present a philosophically sound approach to conceptual model evaluation. Accordingly, the ontological evaluation of conceptual models is enriched with a linguistic interpretivist perspective. The need for such an approach to evaluation is justified by the substantial economic importance of conceptual models. The quality of a conceptual model has a significant impact on other IT artefacts and, thus, on the costs of IT projects. However, little research has so far focused on their evaluation. In the course of this paper, we develop a framework which describes the current state of research and recognizes neglected research fields. With the aid of this framework we identify a notable shortcoming in conceptual model evaluation research, especially with respect to philosophically sound evaluation procedures. Based on these findings we address the following research questions: What are the shortcomings in current evaluation research, what are the merits of ‘ontological evaluation’ in this context, and how can the linguistic interpretivist approach help to form a comprehensive and philosophically sound conceptual model evaluation approach

Development of a data model for an Adaptive Multimedia Presentation System (AMPS)

We investigate the requirements and nature of data models for a multimedia learning system that presents adaptable learning objects based on a range of stimuli provided by the student and tutor. A conceptual model is explored together with a proposal for an implementation using the well-known relational data model. We also investigate how to describe the learning objects in the form of hierarchical subject ontology. An ontological calculus is created to allow knowledge metrics to be constructed for evaluation within data models. We further consider the limitations of the relational abstract data model to accurately represent the meaning and understanding of learning objects and contrast this with less structured data models implicit in ontological hierarchies. Our findings indicate that more consideration is needed into how to match traditional data models with ontological structures, especially in the area of database integrity constraints

Ontological Clarity, Cognitive Engagement, and Conceptual Model Quality Evaluation: An Experimental Investigation

When analysts build information systems, they document their understanding of users’ work domains via conceptual models. Once a model has been developed, analysts should then check it has no defects. The literature provides little guidance about approaches to improve the effectiveness of conceptual model quality evaluation work. In this light, we propose a theory in which two factors have a material impact on the effectiveness of conceptual model quality evaluation work: (a) the ontological clarity of the conceptual models prepared, and (b) the extent to which analysts use a quality evaluation method designed to cognitively engage stakeholders with the semantics of the domain represented by a conceptual model. We tested our theory using an experiment involving forty-eight expert data modeling practitioners. Their task was to find as many defects as possible in a conceptual model. Our results showed that participants who received the conceptual model with greater ontological clarity on average detected more defects. However, participants who were given a quality evaluation method designed to cognitively engage them more with the semantics of the domain did not detect more defects. Nonetheless, during our analysis of participants’ protocols, we found that those who manifested higher levels of cognitive engagement with the model detected more defects. Thus, we believe that our treatment for the level of cognitive engagement evoked by the quality evaluation method did not take effect. Based on our protocol analyses, we argue that cognitive engagement appears to be an important factor that affects the quality of conceptual model evaluation work

An Initial Empirical Assessment of an Ontological Model of the Human Genome

Conceptual modeling is used to model application domains for which an information system is needed. One of the most complex domains to which conceptual modeling has been applied is that of the human genome. Due to its complexity, its understanding is often left to domain experts. Conceptual models represent genomics-related concepts, with various purposes, including domain clarification or data structures design for facilitating data integration. However, traditional conceptual models, which might be expressed, for example, with UML, may not be appropriate for properly explaining such a complex domain, thus requiring an additional layer to ground the model on well-accepted ontological foundations. To achieve this result, an “ontological unpacking” method has been proposed that uses OntoUML as a visual formalism. In this research, we carry out an empirical study to compare the two mentioned representations. The study involved a small group of participants, who responded to a set of questions by reading either a UML model or its related OntoUML unpacked version; the results enabled us to assess their understanding of the domain. We aim to initiate a practical evaluation framework to assess the effectiveness, efficiency and user beliefs of models derived by ontologically unpacking traditional conceptual models. The results of the analysis provide the basis for a broader assessment

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

Construct redundancy in process modelling grammars: Improving the explanatory power of ontological analysis

Conceptual modelling supports developers and users of information systems in areas of documentation, analysis or system redesign. The ongoing interest in the modelling of business processes has led to a variety of different grammars, raising the question of the quality of these grammars for modelling. An established way of evaluating the quality of a modelling grammar is by means of an ontological analysis, which can determine the extent to which grammars contain construct deficit, overload, excess or redundancy. While several studies have shown the relevance of most of these criteria, predictions about construct redundancy have yielded inconsistent results in the past, with some studies suggesting that redundancy may even be beneficial for modelling in practice. In this paper we seek to contribute to clarifying the concept of construct redundancy by introducing a revision to the ontological analysis method. Based on the concept of inheritance we propose an approach that distinguishes between specialized and distinct construct redundancy. We demonstrate the potential explanatory power of the revised method by reviewing and clarifying previous results found in the literature

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: 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

Developing Ontological Theories for Conceptual Models using Qualitative Research

Conceptual modelling is believed to be at the core of the IS discipline. There have been attempts to develop theoretical foundations for conceptual models, in particular ontological models as axiomatic reference systems. Although the notion of ontology has become popular in modelling theories, criticism has risen as to its philosophical presuppositions. Taking on this criticism, we discuss the task of developing socially constructed ontologies for modelling domains and outline how to enhance the expressiveness of ontological modelling theories by developing them via qualitative research methods such as Grounded Theory