Business Systems Analysis With Ontologies

Business Systems Analysis with OntologiesPeter Green University of Queensland, Australia Michael Rosemann Queensland University of Technology, Australia

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Published in the United States of America by Idea Group Publishing (an imprint of Idea Group Inc.) 701 E. Chocolate Avenue Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.idea-group.com and in the United Kingdom by Idea Group Publishing (an imprint of Idea Group Inc.) 3 Henrietta Street Covent Garden London WC2E 8LU Tel: 44 20 7240 0856 Fax: 44 20 7379 3313 Web site: http://www.eurospan.co.uk Copyright 2005 by Idea Group Inc. All rights reserved. No part of this book may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this book are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Business systems analysis with ontologies / Peter Green and Michael Rosemann, editors. p. cm. Summary: "This book shows systems analysts and business analysts how ontological thinking can help them clarify requirements analysis tasks in business systems"--Provided by publisher. Includes bibliographical references and index. ISBN 1-59140-339-1 (h/c) -- ISBN 1-59140-340-5 (s/c) -- ISBN 1-59140-341-3 (ebook) 1. Industrial management--Data processing. 2. Information resources management. 3. Ontology. I. Green, Peter, 1958- II. Rosemann, Michael, 1967HD30.2.B879 2005 658'.001--dc22 2004029772 British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher.

Business Systems Analysis with OntologiesTable of ContentsPreface ............................................................................................................. vi Peter Green, University of Queensland, Australia Michael Rosemann, Queensland University of Technology, Australia Introduction: Setting the Scene ................................................................. xii Yair Wand, The University of British Columbia, Canada Ron Weber, Monash University, Australia Chapter I Ontological Analysis of Business Systems Analysis Techniques: Experiences and Proposals for an Enhanced Methodology .................... 1 Peter Green, University of Queensland, Australia Michael Rosemann, Queensland University of Technology, Australia Chapter II Evaluating Conceptual Modelling Practices: Composites, Things, Properties ...................................................................................................... 28 Graeme Shanks, Monash University, Australia Jasmina Nuredini, Monash University, Australia Ron Weber, Monash University, Australia Chapter III Ontological Analysis of Reference Models .............................................. 56 Peter Fettke, Johannes Gutenberg University Mainz, Germany Peter Loos, Johannes Gutenberg University Mainz, Germany

Chapter IV Thinking Ontologically: Conceptual vs. Design Models in UML ........ 82 Jrg Evermann, Victoria University of Wellington, New Zealand Chapter V Template-Based Definition of Information Systems and Enterprise Modelling Constructs ............................................................................... 105 Andreas Opdahl, University of Bergen, Norway Brian Henderson-Sellers, University of Technology, Sydney, Australia Chapter VI A Reflective Meta-Model of Object-Process Methodology: The System Modeling Building Blocks ................................................. 130 Iris Reinhartz-Berger, University of Haifa, Israel Dov Dori, Technion, Israel Institute of Technology, Israel Chapter VII Ontology-Driven Method Engineering for Information Systems Development .............................................................................................. 174 Roland Holten, University of Frankfurt, Germany Alexander Dreiling, Queensland University of Technology, Australia Jrg Becker, European Research Center for Information Systems, Germany Chapter VIII Using a Common-Sense Realistic Ontology: Making Data Models Better Map the World .............................................................................. 218 Ed Kazmierczak, The University of Melbourne, Australia Simon Milton, The University of Melbourne, Australia Chapter IX Applying the ONTOMETRIC Method to Measure the Suitability of Ontologies .............................................................................................. 249 Asuncin Gmez-Prez, Politcnica University of Madrid, Spain Adolfo Lozano-Tello, Extremadura University, Spain

Chapter X A Twofold Approach for Evaluating Inter-Organizational Workflow Modeling Formalisms ............................................................................... 270 Benoit A. Aubert, HEC Montreal and CIRANO, Canada Aymeric Dussart, Robichaud Conseil and CIRANO, Canada Michel Patry, HEC Montreal and CIRANO, Canada Chapter XI Methodological Issues in the Evaluation of System Analysis and Design Techniques .................................................................................... 305 Andrew Gemino, Simon Fraser University, Canada Chapter XII Ontological Foundations of Information Systems Analysis and Design: Extending the Scope of the Discussion ................................... 322 Boris Wyssusek, Queensland University of Technology, Australia Helmut Klaus, Queensland University of Technology, Australia Chapter XIII Some Applications of a Unified Foundational Ontology in Business Modeling ..................................................................................................... 345 Giancarlo Guizzardi, University of Twente, The Netherlands Gerd Wagner, Brandenburg University of Technology, Cottbus, Germany About the Authors ..................................................................................... 368 Index ............................................................................................................ 377

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Preface

Ontologies are not a philosophical topic only anymore. For more than 10 years now, researchers in different streams related to information technology have been interested in applying sound ontological foundations to their work. An increasing number of special issues of journals, conference sessions and workshops have been dedicated to the application of ontologies in information systems (IS) and computer science. The best paper at the International Conference of Information Systems (ICIS) 2002 applied an ontology to UML and established academic events such as CAiSE and the ER-Conference include a significant number of papers related to ontologies now. This immense popularity of ontologies hopefully will further contribute to the theoretical foundations of the disciplines of information systems and computer science. However, the popularity also means that we have to be even more careful with our references to ontologies. Already, the type of research work that is conducted under the umbrella term ontologies varies significantly. Academics working on the semantic Web, knowledge management, E-business or natural language processing develop, compare, and apply ontologies. However, the understanding of the characteristics of ontology in terms of its scope, details or purpose varies significantly. In 2004, we guest-edited a special issue of the Journal of Database Management titled, Ontological Analysis, Evaluation and Engineering of Business Systems Analysis Methods. It covered the applications of ontologies in the context of methods, techniques and grammars for the purposes of business and systems engineering. Business systems analysis (BSA) grammars were deemed to include data modelling, process modelling and object-oriented modelling techniques. Ontologies are seen as a promising theoretical platform that might be able to provide a valuable reference for the evaluation of the tremendous number of grammars that have been already developed. In that special issue, we were very interested in new results of ontological analyses of different BSA

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grammars. Other areas of interest were further theoretical guidance for the process of ontological evaluations of BSA grammars, documentation of ontologies with relevance to the BSA community, or the selection of appropriate ontologies in the first place. Our call for chapters for that special issue received in excess of 15 full chapters from authors in nine different countries. Because of the obvious interest and sound, diverse work in the area, we decided to extend the concept of the special issue and approach Idea Group Publishing about producing an edited research book pulling together more fully the excellent work that is being done by colleagues worldwide in the areas of ontological comparison, evaluation and analysis. We have titled this book Business Systems Analysis With Ontologies. This title reflects the profound influence that the science of ontological analysis and evaluation is having on the development of the grammars, techniques and tools being used by academics and practitioners alike in business systems analysis. We are excited that two of the thought leaders in the development and application of an IS-related ontology provided insights into their current perspective on this topic in the introduction of the book. Yair Wand and Ron Weber outline in Setting the Scene how and why they see theories of ontology being important to the information systems field generally, and particularly, to the area of modelling. Moreover, Wand and Weber are enthused by the work in the area when they maintain, in the introduction of this book, Conceptual modelling is not a defunct, arcane activity. Rather, in our view it remains a vibrant, central element of information systems development and implementation work. In Chapter 1, Green and Rosemann reflect on their experiences with the application of the BWW models. Their chapter discusses typical problems in the use of any ontology in the context of business systems analysis. Furthermore, it expands particularly on the problems involved in the process of ontological analysis. The authors propose an enhanced procedural model for the ontological analysis based on the use of meta-models, the involvement of more than one coder and metrics. An overview about previous ontological evaluations of BSA grammars also demonstrates the scope of the related research. Chapter 2 by Shanks, Nuredini, and Weber provides an excellent summary of three years worth of experimental work into how alternative conceptual modelling representations affect end-user understanding of these representations. The researchers find evidence to support better end-user understanding when part-whole relations, things, and properties of things are represented in an ontologically-sound manner. Furthermore, they use a process-tracing technique to explain why the ontologically-sound representation of things and properties is more easily understood. Fettke and Loos, in Chapter 3, begin demonstrating how widely ontological analysis can be applied in the general area of business systems analysis. These authors turn their minds to the analysis of reference models. Reference models

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are commonly provided, for example, in enterprise resource package software to provide a starting point by which businesses can understand the business processes that are presumed in the software. Accordingly, their chapter focuses on evaluation of reference models based on a sound theory, namely the ontology proposed by the BWW model. They apply their approach to some parts of Scheers reference model for production planning and control. The results demonstrate that the modelling grammar used to represent the reference models has ontological deficiencies. These deficiencies lead to several problems in the reference model, for example the meaning of some modelling constructs is vague and some aspects of a reference model are redundant. In Chapter 4, Evermann explores the idea that languages such as UML currently used for conceptual modelling possess no real-world business or organizational meaning. His chapter discusses how such meaning can be assigned to languages like UML. It provides an example that demonstrates the differences between a software design model and a conceptual model in UML. He demonstrates how ontology can assist the modeller to not confuse software aspects with aspects of the real world being modelled. Opdahl and Henderson-Sellers have used an ontology, the BWW representational model, as a basis for developing a template for defining enterprise and IS modelling constructs in a way that facilitates language integration. In their Chapter 5, they have clarified the template further by formalising the meta-model through semi-formal constraints expressed in the object constraint language (OCL) and by populating the meta-model with definitions of example constructs from the UML version 1.4. The purpose was to make the template easier to understand, to validate the template, to pave the way for stronger tool support for the template, and to further our work on providing a complete, template-based definition of the UML. The authors of Chapter 6 focus on the ontologically complete object process model (OPM) for conceptual modelling. A comprehensive reflective meta-model of OPM is presented, using a bimodal representation of object-process diagrams and object-process language paragraphs. The meta-model of the UML industry standard depicts only the language part, leaving the (software or any other) system development processes informally defined as a unified process. In sharp contrast to this, OPM, being an object-process approach, enables reflective meta-modelling of the complete methodology, including its language (with both its conceptual-semantic and notational-syntactic aspects) and the OPM-based system development process. This ability to create a reflective meta-model of OPM is indicative of OPMs expressive power, which goes hand in hand with OPMs ontological completeness according to the Bunge-WandWeber (BWW) evaluation framework. Holton, Dreiling, and Becker, in Chapter 7, have used several philosophical and linguistic foundations, such as Kamlah and Lorenzens language critique approach, Morris findings on semiotics, de Saussures findings on signs, and

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Bunges research in ontology to produce an ontology-driven method for information system development. The authors show that ontologies are created and maintained by language communities using linguistic actions and how new concepts can be created to handle new situations. Furthermore, they demonstrate their ontology-driven method to information systems development by introducing an ontology for the domain of management information systems. Chapter 8 begins work on the vexed question of which ontology to use as a basis for the analytical and evaluative work on business systems analysis grammars. Only a few ontologies that tend to be more general in nature are popular in the analysis of business systems analysis grammars. One of these ontologies comes originally from the work by Chisholm and it forms the reference for the study by Kazmierczak and Milton. Their chapter and the work reported in it are driven by an interest in the fundamental nature of data modelling languages. In this research, the ontology helps us to understand, compare, evaluate, and strengthen data modelling languages. This work on which ontology to use is continued in Chapter 9 by Gmez-Prez and Lozano-Tello. Many researchers tend to select a familiar ontology rather than carefully evaluating different ontologies. ONTOMETRIC is an adaptation of the AHP method to help knowledge engineers to choose the appropriate ontology for a new project; in order to do this, the engineer must compare the importance of the objectives, and study carefully the characteristics of ontologies. The framework provides a useful schema to carry out complex multicriteria decision-making. However, the evaluators need to specify in detail the aims of their analysis. Aubert, Dussart, and Paltry, in Chapter 10, demonstrate another area of application of ontology within business systems analysis: the semantic specification of inter-organizational workflow. Moreover, their chapter aims at determining if the ontological validity of available formalisms is sufficient to represent workflows crossing organizational boundaries. A review of several formalisms reveals that the UML fulfils essential representation criteria related to B2B workflows. Moreover, it possesses several extension possibilities that make it a powerful and popular language for business modelling. Andrew Gemino, in Chapter 11, provides a refreshing and contrasting point-ofview on the question of the effectiveness of the ontology selected and used as the analytical basis. He reverts to tried and tested economic theory espoused by Friedman to advocate that the first test of any ontology or meta-model is logical completeness and consistency. This should be a relatively objective exercise. Once an ontology or meta-model has passed this logical test, it can then be used to identify differences among modelling techniques. The impact that these differences have on participants can then be hypothesized using cognitive theory and eventually tested empirically. The ontology (meta-model) that is better is the ontology that provides us with differences that lead to useful empirical results.

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The researchers in Chapter 12, Wyssusek and Klaus, take a very philosophical reflection on the process of using an ontology as a basis for analysis, evaluation and development of information systems. The authors try to establish that when dealing with fundamental issues of theory and practice it is advisable to create an awareness of the potential and limitations of our knowing and doing. This entails considering marginalised positions in a critical discussion of approaches toward information systems analysis and design. Finally, in Chapter 13, Guizzardi and Wagner attempt to draw on all the previous research in the area of ontological foundations to produce a unified foundational ontology UFO 0.2. They have stratified UFO into three ontological layers in order to distinguish its core, UFO-A, from the perdurant extension layer UFO-B and from the agent extension layer UFO-C. The researchers claim that, although there is not much consensus yet in the literature regarding the ontology of agents, such an ontology is needed for building the foundation of conceptual business process modelling. We hope that you will enjoy this research book as much as we have enjoyed the work involved in preparing it. May this book and the work reported in it be of guidance and stimulation for your own research.

Peter Green UQ Business School, University of Queensland, Australia Michael Rosemann Queensland University of Technology, Australia

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Acknowledgments

The fact that this book is able to provide a comprehensive, detailed, and current overview about the utilization of ontologies in the context of Business Systems Analysis is due to the excellent contributions we received by academics who are globally perceived as the thought-leaders in this area. We are very grateful to those authors who were willing to revise, update, and extend their papers as they were published originally in our Special Issue (Vol. 15, Nr. 2, 2004) for the Journal of Database Management. Moreover, we are thankful to those authors who followed our invitation and submitted a book chapter. Each chapter in this book has been evaluated by at least two experienced reviewers and carefully revised based on these comments. We are indebted to our international and national colleagues who selflessly provided comprehensive and insightful reviews through which the contributing authors could improve their chapters. We acknowledge the related workloads of all concerned and we believe that this rigorous process contributed significantly to the overall quality of this publication. A particular note of thanks must go to Ron Weber and Yair Wand. Without their original ideas, unflagging support, and exemplary academic professionalism, we would not have been inspired to start and complete this project. Furthermore, we like to express our appreciation for the excellent support we received from IDEA Publishers. It has been a well-managed process that kept the entire endeavor on track at all times. Finally, we like to take the opportunity to dedicate this book to our families, to Barbara, Brendan and Daniel, to Louise, Noah and Sophie, who carry too often the burden of two easily over-committed academics. Peter Green & Michael Rosemann

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Setting the Scene

Introduction:

Ontology is the branch of philosophy that deals with theories about the structure and behaviour of the worlds that humans perceive. Ontologists seek to articulate the fundamental types of phenomena that exist in the world and the relationships that can arise among these different types of phenomena. Ontologies can be proposed at various levels of abstraction. At the most-general level, an ontology articulates the fundamental constructs we need to be able to describe any phenomenon in the world. At any intermediate level, an ontology articulates the constructs needed to describe particular types of phenomena that occur in some domain for example, architecture, law, nursing, and carpentry. At lower levels, ontologies articulate the constructs needed to describe specific worlds for example, the world faced by a particular business as it attempts to survive in a particular context. Why are theories of ontology relevant to the information systems field? The answer is that the essence of an information system is that it is intended to be a faithful representation of a world that a human or group of humans perceives. Theories of ontology provide us with an artifice for describing a perceived world. Our descriptions will only be as good as our ontologies. Accordingly, our information systems will only be as good as our ontologies. In the mid-1980s, we happened on the field of ontology by chance. We were seeking to identify the core the essence of an information system and to determine whether we had any theories of this core (whatever the core might be). After substantial discernment, we had concluded the core pertained to representation of some world. Thus, we began to seek theories that would account for the nature of good and poor representations. In part, work that had been done by semantic modelling researchers seemed relevant. We found this work inherently unsatisfying, however, because it was not grounded in rigorous theory, nor did it seem complete. One of us (Weber) was visiting the University of British Columbia on sabbatical leave at the time. I (Weber) had been allocated an office next to professor Richard Mattesich, who is both an eminent

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accounting researcher and philosopher of science. During a conversation with Mattesich where I explained the fundamental problem that Wand and I were addressing, he simply went to his bookshelf, selected the two volumes of Mario Bunges Treatise on Basic Philosophy that deal with ontology, handed them to me, and suggested I read them. A new world began to unfold for us. We first tried to apply Bunges ontological model to the formal analysis of control and audit procedures in information systems. As our work progressed, we realised ontological theories could be used in several ways. First, ontology provides a set of benchmark concepts to evaluate models used in systems development notably, conceptual models of some application domain. Second, ontology provides a set of concepts to model systems and reason about their characteristics (this was our first use of Bunges ontology). Third, a specialised ontology can be used to define the meaning of information that will be available in an information system. In this latter role, ontologies often have been used in the artificial intelligence and (recently) semantic Web contexts. Since the early 1990s, we are delighted to see that a growing number of researchers have started to use ontological theories as a basis for their work on conceptual modelling. Much has been done. In our view, however, much still remains to be done. For instance, witness the problems currently being faced by researchers who are trying to find ways to model the world that will allow information systems interoperability to be achieved. Indeed, we are convinced that we have only commenced to scrape the surface of an immense, difficult research area. In terms of theory, for example, it is clear that even well-developed ontologies like Bunges need considerable extension and refinement to address the needs of information systems scholars and practitioners. For instance, Bunges ontology provides only a small number of constructs to describe processes albeit fundamental constructs. In contrast, information systems scholars have devised much more-extensive ontologies to describe process phenomena. Unfortunately, these latter ontologies are not always rooted in a sound foundation of more fundamental constructs like things and properties. In short, we see substantial opportunities for philosophers interested in ontology and information systems scholars to work together to develop high-quality, comprehensive ontological theories. In this regard, information technology and its applications have taken the development of ontological theories from an abstruse, esoteric pursuit to an activity with important, high-value practical applications. Theories of ontology also have a curious status. Conventionally, theories provide a means to explain or predict some phenomena. For the most part, however, theories of ontology provide a means of describing rather than explaining or predicting some phenomena. In this light, they function more like a taxonomy than a conventional theory because they provide a set of constructs for classifying and relating phenomena in the world rather than predicting or explaining them. Nonetheless, they still have predictive and explanatory overtones. They

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imply that describing phenomena in the world via the constructs they provide somehow has value. Presumably, if phenomena are classified correctly according to the theory, humans will be better able to understand and predict the phenomena and thus work more effectively and efficiently with the phenomena. This assumption underlies the work we have undertaken to map between ontological constructs and the constructs provided in different conceptual modelling grammars. Our motivation was the recognition that most modelling methods have emerged (and continue to emerge!) without much theoretical grounding. We believe this situation has been a major reason for the proliferation of modelling methods (a phenomenon given the pejorative nickname YAMA yet another modelling method). To the extent a one/one mapping exists between ontological constructs and grammatical constructs, the implication is that conceptual models will somehow be better. For instance, users of conceptual models will be better able to understand them and work more effectively and efficiently with them. In the interests of parsimony, for the most part we have eschewed employing sophisticated psychological and social theories to provide an account of why a one/one mapping between ontological constructs and grammatical constructs is desirable. Like many economic theories, we simply employ broad assumptions about human behaviour in the hope that detailed, complex accounts of why ontological theories are useful can be avoided. Thus, it is an empirical question whether the explanations and predictions we make based on the (usually implicit) assumption that a given ontology reflects the way humans perceive reality are valid. In terms of practice, we have barely begun to explore the implications of ontological theories for how we undertake conceptual modelling work. In this regard, the chapters of this research book provide excellent examples of the sorts of work that might be done. Ultimately, our concern is to build better conceptual models and devise better tools to assist our conceptual modelling work. In our view, to date ontological theories have shown the most potential for informing practice and the design of conceptual modelling tools. For too long, we have proceeded without the benefit of theory. We have designed and built conceptual modelling tools and undertaken conceptual modelling work using too much of a pure engineering strategy construct the artifact and, if we have time, test the artifact. In the absence of good theory, however, we have been unable to predict the likely strengths and weaknesses of our conceptual modelling tools and practices. As a result, we have a mishmash of views of what constitutes good conceptual modelling tools and practice. We also have a large number of different conceptual modelling grammars that have been devised, and the relationships among these grammars are unclear. For instance, if UML is a comprehensive modelling grammar, why is the W3C developing the Web ontology language called OWL? Is UML deficient in some way? If so, how is it deficient? Long ago, many scholars in the conceptual modelling area deplored this

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state of affairs and underscored the need for good theory to inform our work. We believe that finally we are starting to see theory-driven conceptual modelling work, primarily via the articulation of ontological theories. Recently, we have encountered colleagues who argue that work on conceptual modelling (and thus ontologies) is no longer important. With the development of and widespread deployment of enterprise systems and their embedded bestpractice business models, why, they ask, would we bother to build conceptual models of some domain? These arguments are reminiscent of those made about the principles of good programming when fourth-generation languages first appeared. Structured programming precepts, for instance, allegedly were no longer important when fourth-generation languages were used to develop programs. Of course, the disasters that ensued with fourth-generation languages when good programming principles were ignored were an acid reminder that good theory transcends technologies. So it is, we believe, with good conceptual modelling principles, especially in the complex environments of enterprise systems. In such environments, conceptual models enable us to represent both the business and the software in a common way and to compare them. The extent to which misfits arise between the business models employed by an organization and the business models engaged within an enterprise system seems to be a good predictor of the likely success that an organization will enjoy when it implements an enterprise system. Conceptual modelling is not a defunct, arcane activity. Rather, in our view it remains a vibrant, central element of information systems development and implementation work.

Yair Wand The University of British Columbia, Canada Ron Weber Monash University, Australia

Ontological Analysis of Business Systems Analysis Techniques

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Chapter I

Ontological Analysis of Business Systems Analysis Techniques:Experiences and Proposals for an Enhanced MethodologyPeter Green, University of Queensland, Australia Michael Rosemann, Queensland University of Technology, Australia

AbstractFor many years in the area of business systems analysis and design, practitioners and researchers alike have been searching for some comprehensive basis on which to evaluate, compare, and engineer techniques that are promoted for use in the modelling of systems requirements. To date, while many frameworks, factors, and facets have been forthcoming, most of them appear not to be based on a sound theory. In light of this dilemma, over the last 10 years, attention has been devoted by researchers to the use of ontology to provide some theoretical basis for the advancement of the business systems modelling discipline. While the selected ontologiesCopyright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.

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are reasonably mature, it is the actual process of an ontological analysis that still lacks rigour. The current procedure leaves room for individual interpretations and is one reason for criticism of the entire ontological analysis. This chapter proposes an enhanced procedural model for the ontological analysis based on the use of meta-models, the involvement of more than one coder and metrics. This model is explained with examples from various ontological analyses.

IntroductionAs techniques for conceptual modelling, enterprise modelling, and business process modelling have proliferated over the years (e.g., Olle et al., 1991), researchers and practitioners alike have attempted to determine objective bases on which to compare, evaluate, and determine when to use these different techniques (e.g., Karam & Casselman, 1993; Gorla, Pu, & Rom, 1995). Throughout the 80s, 90s, and into the new millennium, however, it has become increasingly apparent to many researchers that without a theoretical foundation on which to base the specification for these various modelling techniques, incomplete evaluative frameworks of factors, features, and facets would continue to proliferate. Furthermore, without a theoretical foundation, one framework of factors, features, or facets is as justifiable as another for use (e.g., Bansler & Bodker, 1993). Ontologies and ontological engineering have received much attention in the business systems analysis and design literature over the last decade. Ontology is a well-established theoretical domain within philosophy dealing with identifying and understanding elements of the real world and their meaning. Given that IS professionals create computer systems that depict a portion of the real world, IS professionals might look to ontology to provide the conceptual underpinning that has been missing for so long from the IS modelling discipline. Wand and Weber (1989, 1990a, 1993, 1995) have adapted an ontology proposed by Bunge (1977) in order to provide a foundation for understanding the process in developing an information system. A popular application area of this ontology has been conceptual modelling. Today however, interest in, and the applicability of, ontologies extend to areas far beyond modelling. As Gruninger and Lee (2002) point out, a Web search engine will return over 64,000 pages given ontology as a keyword the first few pages are phrases such as enabling virtual business, gene ontology consortium and enterprise ontology (p. 13). The usefulness of ontology as a theoretical foundation for knowledge representation and natural language processing is a fervently debated topic at the present time in the artificial intelligence research community (Guarino & Welty, 2002).Copyright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.


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Accordingly, this chapter has two main objectives (Rosemann, Green, & Indulska, 2004). First, we aim to identify comprehensively the shortcomings in the current practice of ontological analysis. The identification of such shortcomings will provide a basis upon which the practice of ontological analysis can be improved. Second, we want to develop several propositions and methodology extensions that enhance the ontological analysis process by making it more objective and structured. There are several contributions that this chapter aims to make. They are based on previous experiences with ontological analyses as well as observations derived from published analyses. First, the work presents a detailed analysis of the actual process of performing an ontological evaluation. The presented work identifies eight shortcomings of the current ontological analysis process that is, lack of understandability, lack of comparability, lack of completeness, lack of guidance, lack of objectivity, lack of adequate result representation, lack of result classification and lack of relevance. Each of the identified shortcomings is classified then as belonging to one of three phases of analysis that is, input, process and output. Second, the chapter presents recommendations on how each of the shortcomings in the three phases can be overcome. The recommendations, among other things, include an extended methodology for the improvement of the objectivity of the analysis, as well as a weighting model that aims to improve the classification of the results of any ontological analysis. This chapter unfolds in the following manner. The next section provides an overview about the basic concepts of applying ontologies for the purposes of evaluating modelling techniques and the related work. The third section identifies eight current shortcomings of ontological analyses of modelling techniques that are classified with respect to the three phases of analysis that is, input, process and output. The fourth section provides recommendations concerning how to overcome the identified shortcomings in each of the three phases. The final section provides a brief summary of this work and outlines future research in this area.

Ontological Analysis of Modeling TechniquesThe ontological analysis of modelling techniques is a popular application of ontologies in information systems. The aim of these analyses is to evaluate the goodness of representations that can be produced by a particular modelling technique from the viewpoint of a selected ontology. The ontology forms in this process the benchmark against which the constructs of the modelling techniques are evaluated.Copyright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.

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Weber (1997) distinguishes the following two major situations that may occur when a modelling technique is analysed in such a way. After a particular modelling technique has been analysed, predictions on the modelling strengths and weaknesses of the technique can be made according to whether some or any of these situations arise out of the analysis. 1. 2. Ontological completeness exists if there is at least one modelling grammatical construct for each ontological construct. Ontological clarity is determined by the extent to which the modelling technique does not exhibit one or more of the following deficiencies:

Construct overload exists in a modelling technique if one grammatical construct represents more than one ontological construct. Construct redundancy exists if more than one grammatical construct represents the same ontological construct. Construct excess exists in a modelling technique when a grammatical construct is present that does not map into any ontological construct.

The popularity of using ontologies as a basis for the analysis of Business Systems Analysis techniques has been growing steadily. The Bunge-Wand-Weber (BWW) ontological models (Weber, 1997), for example, have been applied extensively in the context of the analysis of various modelling techniques. Wand and Weber (1989, 1990b, 1993, 1995) and Weber (1997) have applied the BWW representation model to the classical descriptions of entity-relationship (ER) modelling and logical data flow diagramming (LDFD). Weber and Zhang (1996) also examined the Nijssen Information Analysis Method (NIAM) using the ontology. Green (1997) extended the work of Weber and Zhang (1996) and Wand and Weber (1993, 1995) by analysing various modelling techniques as they have been extended and implemented in upper CASE tools. Furthermore, Parsons, and Wand (1997) proposed a formal model of objects and they use the ontological models to identify representation-oriented characteristics of objects. Along similar lines, Opdahl and Henderson-Sellers (2001) have used the BWW representation model to examine the individual modelling constructs within the OPEN Modeling Language (OML) version 1.1 based on conventional objectoriented constructs. Green and Rosemann (2000) have extended the analytical work into the area of integrated process modelling based on the techniques presented in Scheer (2000a). The BWW models also have been applied in the context of Enterprise Resource Planning (ERP) Systems. Sia and Soh (2002) utilise the BWW models to propose

Copyright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.


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a theoretically grounded framework for assessing the severity of ERP misalignment in organisations. The authors demonstrate the application of the proposed framework by applying it to a hospital case study, in which significant ERP misalignment is identified as a result. Shanks, Tansley, and Weber (2003) utilise the application of the BWW model in order to investigate the representation of part-whole relationships in conceptual modelling grammars. The authors use the BWW model to support their argument for representation of part-whole relationships as entities as opposed to relationships or associations. Their argument is further supported by an empirical study that concludes that using entities to represent part-whole relationships leads to an improvement in the level of the users understanding of the domain. Davies, Green, and Rosemann (2002) demonstrate the potential usefulness of meta-models for comparing and evaluating ontologies.1 The authors focus on the analysis of the meta-models of the BWW representation model and Chisolms Ontology, concentrating on ontological equivalence, depth of structure, and comprehensiveness of scope of the models. The findings of the work revealed that the two models were not completely ontologically equivalent, with the BWW model being more comprehensive in scope and Chisolms Ontology having a deeper structure than that of the BWW model. Davies, Green, Milton, and Rosemann (2004) extend the work to include a detailed discussion of the benefits of the use of meta-models for evaluating ontologies. Fettke and Loos (2003) discuss the process of BWW ontological evaluation of reference models and identify a number of possible application areas. The authors suggest that the proposed method may be used for evaluation of reference models, comparison of two or more reference models, representation of reference models in model repositories, and describing the key characteristics of reference models in order to facilitate selection of appropriate models in specific situations Most recently, Green, Rosemann, Indulska, and Manning (2004) have extended the use of this evaluative base into the area of enterprise systems interoperability using business process modelling languages like ebXML, BPML, BPEL4WS and WSCI. Table 1 provides an overview of the related work performed to date involving the Bunge-Wand-Weber models. Indeed, much of this work has involved evaluations based on Webers (1997) two situations. A mismatch between ontological and modelling constructs however does not necessarily indicate weaknesses of the target modelling technique. Rather, as Rosemann and Green (2002) point out, it could indicate misspecification in the ontology used for the evaluation.


6 Green & Rosemann

Table 1. Ontological analysis of modelling techniques using the BWW modelsBusiness Systems Analysis Grammar Study Wand & Weber (1989) Wand & Weber (1993, 1995) Weber (1997) Sinha & Vessey (1995) Weber & Zhang (1996) Green (1997) Parsons & Wand (1997) Opdahl & Henderson-Sellers (1999) Wand, Storey & Weber (1999) Rosemann & Green (2000) Green & Rosemann (2000) Bodart et al (2001) Green & Rosemann (2002) Sia & Soh (2002) Opdahl & Henderson-Sellers (2002) Rosemann & Green (2002) (UML) (ARIS & UML Class) (ARIS) Traditional Structured DataCentred (LDFD) (ER) (ER) (Relational) (OML) Ontological Ontological Empirical Tests Other Purpose O-O Process Completeness Clarity

(Enterprise Interoperability) (ERP Systems) (ActivityBased Costing)

Davies, Green & Rosemann (2002) Shanks et al (2003) (UML Class)

(Other Ontology)

Davies et al. (2004) Fettke & Loos (2003) Green, Rosemann, Indulska & Manning (2004)

(Other Ontology) (Reference Process Models) (Interoperability Standards)



7

It could be that the ontology is over-engineered. The ontology may include constructs that are not relevant. The ontological analyses of various modelling techniques to date have consistently identified certain ontological constructs that do not have representations in the grammars examined, for example, conceivable state space, conceivable event space and lawful event space. The ontological analyses to date in themselves form an empirical study around this possibility of over-engineering. One conclusion then could be the identification of the need for a reduction in the number of constructs thought to be sufficient and necessary in the ontology. Even if the ontology is not over-engineered, most modelling techniques usually focus on modelling particular aspects of the real-world, for example, statics, dynamics, processes, data, actors, actions, goals and the like. Apparently, the objectives of the modelling grammar need to be taken into account during the ontological analysis. Such work suggests a need for individualization of the ontology by means of not only designing subsets but also specializations of the ontology a focused ontology. Finally, there may be a need for extending the ontology. Weber (1997), for example, has already extended the understanding of the ontological construct, property, by explaining the various types of property, for example, property in general, property in particular. The growing importance of strategic enterprise modelling might lead to the explication of the BWW model to incorporate for example business objectives, strategies, goals or knowledge.

While there may be misspecification in ontologies, such a problem cannot be verified without substantial empirical research based on the theory being performed. In any case, ontology is seen as a potential fruitful theoretical basis on which to perform analyses of modelling techniques. However, while ontological analyses are frequently utilised, particularly in the area of analysing conceptual modelling techniques, the actual process of performing the analysis remains problematic. The current process of ontological analysis is open to the individual interpretations of the researchers who undertake the analysis. Consequently, such analyses are criticised as being subjective, ad hoc, and lacking in relevance. There is a need, therefore, for the systematic identification of shortcomings of the current ontological analysis process. The identification of such weaknesses, and their subsequent mitigation, will lead to a more rigorous, objective and replicable analytical process.


8 Green & Rosemann

Shortcomings of Current Ontological AnalysesAn ontological analysis is in principle the evaluation of a selected modelling grammar from the viewpoint of a defined and well-established ontology. The current focus of ontological analyses is on the bi-directional comparison of ontological constructs with the elements of the modelling technique that is under analysis. Weber (1997) defines ontological clarity and completeness as the two main perspectives of an ontological analysis. Though this type of ontological analysis is widely established, it still has a range of issues. These issues can be categorised into the three main phases of an ontological analysis that is, preparation of the input data, the process of conducting the analysis and the evaluation and interpretation of the results. The first two identified shortcomings refer to the quality of the input data.

Lack of UnderstandabilitySeveral ontologies that are currently used for analysis of modelling grammars have been specified in formal languages. While such a formalisation is beneficial for a complete and precise specification of the ontology, it is not a very intuitive specification. An ontology that is not clear and intuitive can lead to misinterpretations as the involved stakeholders might have problems with the specifications. Furthermore, it forms a hurdle for the application of the ontology as it requires a deep understanding of the formal language in which it is specified. Moreover, it is not only the meta-model and the notation that is used for the specification of the ontology, but also the selected terminology. In our own applications, for example, we realised that elements of the BWW model such as conceivable state space are not self-explanatory to members of the modelling community.

Lack of ComparabilityThe specification of an ontology requires typically a formal syntax that allows the precise specification of the elements and their relationships of the ontology. Consequently, textual descriptions of the ontology in plain English often extend the formal specification. However, even if an ontology is specified in an intuitive and understandable language, the actual comparison with the selected modelling grammar remains a problem. Unless the ontology and the grammar are specified in the same language or a precise mapping of the two languages exists, it will be up to theCopyright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.


9

coder to mentally convert the two specifications into each other, which adds a subjective element to the analysis. Different languages can also lead easily to different levels of detail and further complicate the analysis. In any case, they make a more automated comparison practically impossible. This situation is typical in many previous analyses. The further three shortcomings identified below are related to the process of the ontological analysis and refer to what should be analysed, how it should be analysed, and who should conduct the analysis.

Lack of CompletenessThe first decision that has to be made in the process of an ontological analysis is the scope and depth of the analysis. Even if most ontologies have been discussed for many decades, they still undergo modifications and extensions. It is up to the researcher to clearly specify the selected version of the ontology and the scope and level of detail of the analysis. In our work in the area of Web services standards, for example, it was often not clear what constructs form the core of the selected Web services standard. Two researchers, who conducted independent analyses of the same Web services standard, selected consequently a different number of constructs. Moreover, many ontological analyses solely focus on the constructs of the ontology and the constructs of the grammar, but do not sufficiently consider the relationships between these constructs. The difficulty in clearly specifying the boundaries of the analysis, as well as the limited consideration of relationships between the ontological constructs, lead to a potential lack of completeness.

Lack of GuidanceAfter the scope and the level of detail of the analysis have been specified, it is typically up to the coder to decide on the procedure of the analysis that is, in what sequence the ontological constructs and relationships will be analysed? Currently, there are hardly any recommendations on where to start the analysis. This lack of procedural clarity underlies most analyses and it has two consequences. First, a novice analyst lacks guidance in the process of conducting the ontological evaluation. Thus, the application of ontological analyses is potentially limited to experts in both the selected ontology and the modelling technique. Second, the procedure of the analysis can potentially have an impact on the results of the analysis. Consequently, it is possible that two analyses that follow a different process may lead to different outcomes.


10 Green & Rosemann

Lack of ObjectivityAn ontological analysis of a modelling technique requires not only detailed knowledge of the selected ontology and technique, but also a good understanding of the languages in which the ontology and the grammar are specified. This requirement explains why most analyses are carried out by single researchers as opposed to research teams. Consequently, these analyses are based on the individual interpretations of the involved researcher, which adds significant subjectivity to the results. This problem is further compounded by the fact that, unlike other qualitative research projects, ontological analyses typically do not include attempts to further increase the validity of the results. The five shortcomings identified above have a common flavour in that they heavily depend on the researcher conducting the ontological evaluation. Three further shortcomings have been identified that is, lack of result representation, lack of result classification and lack of relevance. These shortcomings are detailed below and refer to the outcomes of the analysis.

Lack of Adequate Result RepresentationThe results of a complete ontological analysis that is, representation mapping and interpretation mapping, are typically summarised in two tables. These tables list all ontological constructs (first table) and all grammatical constructs (second table) and the corresponding constructs. Such tables can become quite lengthy and are typically not sorted in any particular order. They do not provide any insights into the relative importance of identified deficiencies. Furthermore, the findings are not clustered typically allowing related deficiencies to appear more apparent. In doing such clustering, the relative importance of the related deficiencies is made clearer as well.

Lack of Result ClassificationIt is common practice to derive ontological deficiencies based on a comparison of the constructs in the ontology and the modelling technique. Ontological weaknesses are identified when corresponding constructs are missing in the obtained mapping between the ontology and the technique, or 1-many (or many1 or even many-many) relationships exist. Such identified deficiencies are the typical starting point for the derivation of propositions and then hypotheses. In general, the ontological analysis does not make any statements regarding the relative importance of these findings in comparison with each other. Though this seems to be the established practice, it lacks more detailed insights into theCopyright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.


11

significance of the results. It is expected, however, that the missing support for a core construct of an ontology can be rated higher than a missing corresponding construct for a minor ontological construct or a relationship. This lack of a more detailed statement regarding the significance of a potential shortcoming makes it difficult to judge quickly the outcomes of the results of two different sets of analyses, for example, an ontological analysis of ARIS in comparison with an ontological analysis of UML.

Lack of RelevanceFinally, the results of an ontological analysis should be perceived as relevant by the related stakeholders. However, if an ontological analysis leads, for example, to the outcome that entity relationship models do not support the description of behaviour, then such an outcome needs a clarification. It seems that an ontological analysis has to consider the purpose of the grammar as well as the background of the modeller who is applying this grammar. The application of a high-level and generic ontology does not consider this individual context and there is a danger that the outcomes can be perceived as trivial or non-relevant.

A Reference Methodology for Conducting Ontological AnalysesThe shortcomings identified above motivated the development of an enhanced methodology for ontological analyses. The main purpose of this methodology is to increase the rigour, the overall objectivity and the level of detail of the analysis. The proposed methodology for ontological analyses is structured in three phases that is, input, process and output.

InputThe formal specification of ontologies, together with the differences in the languages used to specify the ontologies and the grammars under analysis, have been classified as issues pertaining to the lack of understandability and comparability. In order to overcome this shortcoming, we have worked on converting existing specifications for our selected ontology to a more commonly used language that is, to a more intuitively understandable meta-model. There are severalCopyright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.

12 Green & Rosemann

motivations for converting current specifications of ontologies into meta-modelbased specifications. First, the development of a meta-model that describes and clarifies the current understanding of the ontological constructs facilitates the use of ontologies in other related areas such as information systems education. Second, a formal meta-model that clearly describes the elements and relationships within an ontology can help to identify inconsistencies and anomalies in an ontology itself. Third, it can be used for the ontological analysis of modelling techniques (grammars) that are specified in the same metalanguage. In this case, the analysis turns into a pattern matching exercise. Fourth, a meta-model can be used to improve existing techniques and derive new modelling techniques (i.e., ontology-based method engineering). Fifth, it can also be applied for the comparison of different ontologies, if they are specified in the same metalanguage (Davies et al., 2002). Finally, based on the outcomes of the evaluation and comparison of ontologies, a meta-model can be used to develop and specify a new ontology. Figure 1 outlines these application areas for a meta-model of ontological constructs. In order to overcome the lack of understandability and comparability, the first step is to convert the ontology, as well as the selected modelling grammar, to meta-models using the same language (e.g., ER models or UML class diagram). This conversion facilitates a pattern-matching approach towards the ontological analyses of completeness and clarity of a grammar. We converted the BungeWand-Weber ontology into an ER-based meta-model. This meta-model includes 50 entity types and 92 relationship types. It has clusters such as system, property or class/kind. Such a meta-model explains, in a language familiar to the information systems community, the core constructs of the ontology. It also highlights the underlying focus of the ontology. In the case of the BWW model, for example, the visual inspection of the meta-model indicates that the ontology is centred around the existence of a thing, which is the central entity type in the meta-model. Figure 2 provides, as an example, an impression of the size and complexity of the meta-model for the BWW ontology. We used a modern version of the entity relationship (ER) language as the metamodelling language. The version of the ER approach used in our work is based on the original ER specification from Chen (1976) with extensions made by Scheer (2000a). This version is called the extended ER model. This selection was made for the following reasons: 1. Since Chen (1976) introduced the original ER approach, it has undergone intensive discussions and further developments. It is realistic therefore to expect that solutions for special methodological problems that could occur during the process of designing the meta-model are already available in most cases.



13

Figure 1. Application areas of a meta-model for ontological constructs

1a) Facilitates communication about the ontology 2) Clarifies inconsistencies and anomalies

1b) Simplifies teaching the ontology 5) Streamlines the comparison of ontologiesOntology B

QMeta Model for ontological constructs

3) Streamlines the ontological analysis of grammarsGrammar A

Ontology C

Grammar B

4) Enables ontology-based method engineeringNew Grammar

6) Enables ontology

engineeringNew Ontology

Figure 2. The BWW meta-modelB W W m eta m o de l v e r4 - 2 3 /9 /20 0 2 A ut ho r : Is la y D a v ie sm ade up of R eal W o r ld 1,n

SYS TE M

T H I N G / C L A S S / K IN Das s o c i a te d i n to 2,n 0,n C o m p o s i te Th i n g 0,n

P R O P ER TY

has

0,n

S y s te m S t r u c tu r e

1,n has

d,t 1,n S im p l e Th i n g 0,n obs erv ed as d,t P r op e r ty i n general 0,n

1,n

is subset of P r op e r ty i n p a r t ic u la r ca u s e s

0,1

c on t ain s i nit ia t e d by 1,n is n o t i n c l d e d in u 0,n 1,n 1,n a ffe c t e d by

in h e re n t ly p o s se s s e s

1,n I n tr in si c P r op e r ty

n,p

has

S y s te m E n vi ro n m e n t

has

2,n 2,n s hares M u tu a l P r op e r ty d,t B in d in g M u tu a l P r op e r ty

1,n

1,n 1,n

0,n

i n te r a c ts w it h 1,n U n s ta b l e S ta t e 1,n 1,n oc c urs on d,t 1,n 1,n

not a ffe c t e d by H e r e d it a r y P r op e r ty 1,n

2,n N o n - b in d in g M u tu a l P r op e r ty

is subset of

0,n E x te r n a l E ve n t

1,n

0,n E mer gent P r op e r ty 1,n 0,n bel ongs to

i n h e ri te d by

is subset of

d oe s n o t o c cu r o n remai ns d,t x1 + x 2 = 1 ; x1 , 2 i s e le m e n t o f { 0 , 1 } produc es x1,n x2,n S ta b l e S ta t e 1,n

bel ongs to

i nit ia t e d by 1,n 0,n x6,n 0,n generat es 0,n has S y s te m C o m p o s i ti o n 1,n has 0,n I n te r n a l E ve n t 1,n oc c urs on 0,n is i n c l d e d in u 1,n 1,n 0,n 2,n x5,n 0,n x5 + x 6 = 1 ; x5 , 6 i s e le m e n t o f { 0 , 1 } m akes up

m akes up

2,n

K in d

2,n

2,n

0,1

2,n

assum es

C la s s

1,n

1,n c h ar a c te r iz e d by

di s p la y s

c on t ain s is subset of

assum es

1,1 S y s te m 0,1 1,1 1,n 1,1 1,1

x3 + x 4 = 1 ; x3 , 4 i s e le m e n t o f { 0 , 1 } 0,n 0,n 2,n i s d e fin e d as x4,n x3,n 0,n 0,n 1,1 0,n 0,n 0,n 0,n 0,n C oupl ng i 0,n Th i n g 0,n 0,n 0,n 1,n 1,n 1,1 0,n 0,n 0,n 0,n 1,n

c h ar a c te r iz e d by

di s p la y s

0,n 2,n 1,n 0,1 L e ve l S t r u c tu r e f o rm e d by I n te r n a l C oupl ng i d,t

0,n 0,n

0,n

0,n

decom posed i n to

p a r t ia l o r d e r d e f in e d in

1,n

1,n

1,n

P r op e r ty 0,n

1,n 1,1 0,1 1,1 S ub s y s t e m

1,n 1,n

E x te r n a l C oupl ng i

m o d e ll e d as

E V E N T / T R A N S F O R M A T IO N

E ve n t

1,n 1,1 1,n can occur on 1,1

oc c urs on 1,1 2,2

ST A TE

1,n p o s se s s e s 0,n

0,n 0,n

r e p r e s e n te d by

A tt ri b u te

1,n

C ro s s P ro d u c t

1,1

f orms c o n t a in e d in

c o n t a in e d in 'h a s ' r e la t io n s h i p = 'se t o f in d i vid u a l s t a te f u n c ti o n s '

0,n

0,n 1,n

K EY : M o d el o b je c ts :

L a w fu l E ve n t S pace

1,1

is subset of

0,1

C o n c e iva b le E ve n t S pace

0,n

com posed of

1,n

has

0,n V a lu e

f orms c o n s is ts of c o n s is ts of 0,n 0,n 0,n 0,n T r a n s fo r m a t io n 1,1 can assum e 0,n rec orded in c o n t a in e d in assum es 'a s s u m e s ' r e la t io n s h ip = 't o t al s t a t e f un c t io n ' 1,n 1,n 1,1 0,1 1,1 0,1 oc c urs at 1,n Ti me I n s ta n t

1,n

= B W W co re co n s tr uct s

is

0,n 1,n prec edes 1,n 0,n a f fe c t s 0,n

0,n

has

S ta t e

K now n S ta t e

= o th e r B W W c o ns tru c t s

1,n n,p H is t o ry 0,n o rd e r e d by

P re d i c ta b l e S ta t e s uc c e e d s 0,n 0,n

= no n -B W W - co ns t ru c t e nt ity ty p esW e l l- d e fi n e d E ve n t 1,n is

0,n

0,n

c o n t a in e d in d,t

= n o n - BW W -co n st r uc t r e la t io ns h ip t y p esP o o rl y -d e fin e d E ve n t 1,n is not 1,n L a w fu l S ta t e S pace 1,1 0,n is subset of 0,n C o n c e iva b le S ta t e S pace 1,n

= int r o du c e d c o n ce p ts o r ter m s n o t e x plic it ly s t a te d in W e be r ( 1 9 9 7) o r G r e en & R o se m a n n ( 2 0 0 0 ) , w h ic h h a v e be e n a d d ed in o r d er to fu lly de sc ri b e th e B W W c o n s tr u c ts w ith in th e c o n s tr a in ts o f eE R m o de lling

is a r e q u ir e s

c o n t a in e d in

0,n 0,n

1,n

C ro s s P ro d u c t

0,n

G e ne r a liza tio n /Sp e c ia liza t io n: d = d is jo in t (X O R ) n = n o n -d is jo in t (in clus iv e O R ) t = to t a l (a ll s ub ty p es w h ic h e x is t a r e dip ic t ed ) p = p a rtia l (fu rt he r su b t y p es n o t de p ict e d in th e m o d el e x is t)

0,n T r a n s fo r m a t io n L aw 0,n a l lo w e d by 1,1

1,n 1,n 0,n enabl es 0,n

1,n 0,n enabl es 0,n

1,1 a l lo w e d by 0,n

V a lu e C hange 1,n

S ta t e L aw 0,n

d,t

N a tu r a l L aw

0,n

1,1

speci i ed f by 1,n H uman L aw

C o rr e c t iv e A ct io n 1,n

in s ti g a te d by 1,n 1,1 speci i es f 0,n S t ab i lit y C o n d it io n 0,n 0,n speci i ed f in speci i es f speci i ed f in

L a w fu l T r a n s fo r m a t io n 1,1

0,n

1,n

is subset of


14 Green & Rosemann

2. 3.

Even though many potential meta-languages are available, the ER approach is widely accepted as a de facto standard for (data) modelling. Several meta-models based on the ER approach are already available (e.g., ARIS [Scheer, 2000b]) and object-oriented schemas).

The obtained meta-model can be used now for a variety of ontological analyses. Moreover, it allows a critical review of the BWW model by a wider community. The approach, however, is not without its limitations. Commonly used modelling techniques, such as ER or UML, are often widely accepted, but they have not been designed for the purposes of meta-modelling. Thus, they occasionally lack the required expressiveness. While an ER-based meta-model helps to overcome issues related to the understandability of an ontology, a corresponding meta-model of the analysed grammar is required to deal with the lack of comparability issue. Many popular modelling techniques (e.g., ARIS or UML, and also interoperability standards such as ebXML) are already specified in meta-models using ER-notations or UML class diagrams. If the meta-models for the ontology and the modelling technique are specified in the same language, the ontological analyses turns into a comparison of two conceptual models. As part of the analyses, corresponding entity types and relationship types in both models need to be identified. It also becomes immediately obvious whether the focus of the analysed grammar differs from the ontology. In the case of ARIS or many Web services standards, for example, the meta-models are centred around functions or activities instead of being centred around things. As an example of constructs from a particular ontology, Table 2 provides some core ontological constructs defined in plain English and adapted to the IS discipline by Wand and Weber (1995). An extract of the meta-model for a set of selected BWW constructs is described in Figure 3. All object types in this model described as nouns correspond with constructs in the BWW representation model. The basic elements in the BWW representation model are things and their properties. Every thing possesses at least one property and every property belongs to at least one thing. Consequently, a mutual existential dependency exists. Things often consist of other things or they are part of other things. These composite things can be depicted by a recursive relationship type. While thing, composite thing, and property exist in the real world, for modelling purposes, it is necessary to define ways of concentrating the focus in order to reduce complexity. Things together with their properties can be classified in classes by identifying a characteristic property that all the involved things have in common. Each class has at least one relationship to a thing-property couple. Classes (e.g., human beings) may possess subtypes (e.g., man and woman)Copyright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.


15

Table 2. Core ontological constructs in the BWW representation modelOntological Construct THING* Explanation A thing is the elementary unit in the BWW ontological model. The real world is made up of things. Two or more things (composite or simple) can be associated into a composite thing. Things possess properties. A property is modelled via a function that maps the thing into some value. For example, the attribute weight represents a property that all humans possess. In this regard, weight is an attribute standing for a property in general. If we focus on the weight of a specific individual, however, we would be concerned with a property in particular. A property of a composite thing that belongs to a component thing is called an hereditary property. Otherwise it is called an emergent property. Some properties are inherent properties of individual things. Such properties are called intrinsic. Other properties are properties of pairs or many things. Such properties are called mutual. Non-binding mutual properties are those properties shared by two or more things that do not make a difference to the things involved; for example, order relations or equivalence relations. By contrast, binding mutual properties are those properties shared by two or more things that do make a difference to the things involved. Attributes are the names that we use to represent properties of things. The vector of values for all property functions of a thing is the state of the thing. A transformation is a mapping from one state to another state. A stable state is a state in which a thing, subsystem, or system will remain unless forced to change by virtue of the action of a thing in the environment (an external event).

PROPERTY*: IN GENERAL IN PARTICULAR HEREDITARY EMERGENT INTRINSIC NON-BINDING MUTUAL BINDING MUTUAL ATTRIBUTES

STATE* TRANSFORMATION* STABLE STATE*

Figure 3. Thing, property, class, kind, attributeT h in g 1 ,n C la sse p ossess s 0 ,n 1 ,n P rop e rty 0 ,n

0 ,n

0 ,n C o m p osite T h in g C h ara cte ristic P rop e rty is m o d elle d as

1 ,n K in d 1 ,1 is a 0 ,1 C la ss

1 ,n A ttrib u te

called kinds. Through attributes the context-relevant properties can be modelled and they become more easily understood. In contrast, an attribute requires the existence of at least one property, as it cannot exist on its own. The development and applicability of the full meta-model is reported in Rosemann and Green (2002).Copyright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.

16 Green & Rosemann

Figure 4. Comparison of the BWW meta-model and the ARIS meta-modelBWW Model 0,n

precedes

0,n 0,n

Transformation 0,n

State

succeeds

ARIS 0,1

precedes

0,1 0,1

Function

0,1

Event

succeeds

Figure 4 depicts an example that shows how meta-models can facilitate the ontological analysis of a modelling grammar. The excerpt of the BWW metamodel depicts the dynamic part that constitutes a process in which states and transformations are strictly alternate. Both constructs together form, in the terminology of the BWW models, an event. The bottom portion of Figure 4 includes the corresponding part of the meta-model of the Architecture of Integrated Information Systems (ARIS). In the modelling technique, eventdriven process chains (EPC), of ARIS, each process consists of an alternate sequence of events and functions. Thus, functions (events) of the EPC modelling technique can be mapped to the transformations (states) of the BWW models. Corresponding mappings are possible for the relationship types. Such a model comparison allows an objective ontological analysis and easily facilitates the identification of weaknesses such as ontological overlap, excess or redundancy (Green & Rosemann, 2000). Furthermore, this approach helps to identify synonyms (e.g., function and transformation) as well as homonyms (e.g., event).

ProcessIssues related to the process of conducting an ontological analysis have been described as lack of completeness, lack of guidance and lack of objectivity. Based on the assumption that corresponding meta-models for the ontology and the analysed grammar are available, it is possible to clearly specify the scope of an analysis using those meta-models. Such a selection of clusters, entity types and relationship types would define all elements that are to be perceived of relevance for a complete analysis. An analysis of an ER-based notation, for example, could be focused on the BWW clusters thing, system, and property



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and could exclude the more behavioural-oriented clusters event and state. Such boundaries of an analysis could be easily visualised in the meta-model and would provide a clear description of the comprehensiveness of the analysis thus, mitigating the completeness criticism. The existence of two corresponding meta-models and a clear definition of the scope of the analysis are necessary, but not sufficient, criteria for a well-guided process. Further guidelines are required regarding the starting point of such a process and the actual sequence of activities. Based on our experiences, we recommend starting with the representation mapping that is, selecting the meta-model of the ontology and subsequently identifying the corresponding elements in the modelling grammar. The first construct to be analysed should be the most central entity type that is, in the case of the BWW models the entity type thing. Our previous work provides a strong argument that this analysis should be followed by a cluster-by-cluster approach. Starting with the core constructs in a cluster, this approach allows a more structured and focused analysis of the completeness of a modelling grammar. The analysis of the entity types is followed by the relationship types and the cardinalities. Constructs in the meta-model that only have been introduced for the correctness of the metamodel, but that do not reflect ontological constructs are excluded from the analysis. The representation mapping is followed by an analysis of the clarity that is, the interpretation mapping. In this case the meta-model of the grammar under analysis is the starting point. The general procedure is similar. A main advantage of a cluster-based analysis is that the structure of the two metamodels provides valuable input for the ontological analysis. In addition to the cluster-based analysis, a further guideline in the process relates to generalisation-specialisation relationships in the meta-model of the grammar. We propose to classify ontologically the super-type first and then to inherit this ontological classification to all sub-types. These guidelines streamline the process of the analyses and increase the consistency. The lack of objectivity issue, on the other hand, stems frequently from the analysis being performed by a single researcher. The situation results in an analysis that is almost certainly biased by the researchers background as well as their interpretation of the specification of the grammar. In order to improve the validity of the analysis, a research methodology can be adopted that undertakes individual analyses of a particular grammar by at least two members of a research team, followed by consensus as to the final analysis by the entire team of researchers. The methodology consists of three steps: 1. Using the specification of the grammar in question, at least two researchers separately read the specification and interpret, select and map the ontological constructs to candidate grammatical constructs to create individual first drafts of the analysis.


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2.

The researchers involved in step 1 of the methodology meet to discuss and defend their interpretations of the modelling technique analysis. A concurrence score is determined then from their initial analyses. This meeting leads to an agreed second draft version of the analysis that incorporates elements of each of the researchers first draft analyses. The overlap in the selection of the constructs and in the actual ontological analysis can be quantified by concurrence/agreement scores that are used in content analysis and other more qualitative research. The second draft version of the analysis of the modelling technique is used as a basis for defence and discussion in a meeting involving the entire research team. The outcome of this meeting forms the final analysis of the grammar in question.

3.

Such a methodology was employed in a project that sought to apply the BWW representation model analysis to a number of the leading potential Web service standards that is, ebXML, BPML, BPEL4WS, and WSCI. The project team was composed of four researchers and the standards were analysed in the order: ebXML BPML BPEL4WS WSCI. Two researchers were involved in steps 1 and 2 of the methodology that is, the individual analysis of a standard followed by a meeting of the two researchers in order to obtain an agreed mapping. This phase was followed by a meeting of the entire team in order to discuss the mapping and arrive at the final analysis. The process was performed for each of the four standards. Table 3 shows the recorded agreement statistics at the second step of the applied methodology, while Table 4 shows the recorded agreement statistics at the third step of the methodology. Meta data of the ontological analysis such as the mapping ratio provides valuable information in addition to the actual outcomes of the analysis. In the case of the analysis of the Web services standards, for example, these figures give insight into how difficult or easy these standards are to understand. The adoption of such a methodology is seen to have improved significantly the objectiveness of the analyses.

Table 3. Summary of step 2 mapping agreement between both researchersWeb Service Language ebXML BPML BPEL4WS WSCI Representation Mapping agreed upon by both researchers 43 36 30 39 Total number of specification constructs identified 51 46 47 49 Mapping ratio

84% 78% 63% 79%



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Table 4. Summary of step 3 mapping agreementWeb Service Language ebXML BPML BPEL4WS WSCI Representation Mapping agreed upon by the team 49 41 42 46 Total number of specification constructs identified 51 46 47 49 Mapping ratio

96% 89% 89% 94%

OutputThe three main shortcomings related to the outcome of an ontological analysis have been characterised as the lack of adequate result representation, lack of result classification and the lack of relevance. The meta-models that have been used as input for the ontological analyses are also an appropriate medium to visualise the outcomes of the entire analysis process. In our work on the analysis of ARIS, we derived a meta-model of the BWW model that highlighted all constructs of the ontology that do not have a corresponding construct in the grammar under analysis that is, we visualised incompleteness in the model using simple colour coding. In a similar way, we derived three ARIS meta-models that highlighted excess, overload and redundancy in ARIS. Such models form a very intuitive way of representing the identified ontological shortcomings. The underlying clustering of the models also helps to quickly comprehend the main areas of shortcomings. At the present time, the process of an ontological analysis results in the identification of ontological incompleteness and ontological clarity through the identification of missing, overloaded or redundant grammatical constructs. While the end result identifies such problems, it fails to account for their relative importance. For example, thing is one of the fundamental constructs of the BWW model. The lack of mapping for the construct should, therefore, be considered more important than the lack of mapping for the well-defined event construct for example. There is a need for the development of a scoring model that enables the calculation of the goodness of a grammar with respect to the ontology. In such a scoring model, each of the ontological constructs has a value assigned to it that reflects the relative importance of the construct in the ontology. Core constructs would therefore have high weightings whereas less important constructs would attract lower values of weightings. Following an ontological analysis of a particular grammar, the weighting of all missing constructs would be calculated to arrive at one value that generally reflects the outcome of the analysis.


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An example for such a classification could have for example the following structure. All core constructs of an ontology (and the modelling grammar) would get the value 1. All other constructs represented as an entity type in the metamodel of the ontology would receive the value 0.7, and all remaining constructs get the value 0.3. Such a weighting would then be applied to the outcomes of the ontological analysis. The scores would be aggregated across the ontology and modelling grammar. They also would be calculated separately for completeness, excess, overload and redundancy. Furthermore, they could be aggregated per cluster that allows a more differentiated view on the particular strengths of a modelling grammar. Though the consolidated score of such an evaluation should not be overrated, it provides better insights into the characteristics of the ontological deficiencies and provides a first rating of the significance and importance of the identified shortcomings. It can also be used for the design of the subsequent empirical studies. Apart from the lack of result classification that is addressed by the scoring model, another problem with the outcome of the analyses has been the perceived lack of relevance. The merit of a foundational ontology that is, its generic nature and its completeness, can also be seen as a shortcoming the ontology might cover more than what one single modelling technique can support and its level of abstraction is too high in order to form a specific benchmark. Thus, three activities seem to be required in order to convert foundational ontologies into focused ontologies.

First, since most modelling grammars concentrate on modelling a sub-set of the phenomena that occurs in the real world, it would follow that not all constructs of an ontology are necessary in order to analyse such a grammar. If the full ontology is used in the analysis, the result may identify potential problems that would not, in reality, occur, because the modelling grammar is not used to model any phenomena described by the missing constructs. Consequently, a focused ontology can be derived by deleting constructs from the selected ontology. Indeed, the outcomes of the ontological analyses of different modelling grammars to date appear to support the need for a focused ontology that consists of different subsets of the ontological constructs for different domains. The analyses of process modelling grammars consistently show that the constructs conceivable state space, conceivable event space and lawful event space, for example, have no representation constructs in the grammars. Such missing constructs, if identified to be unnecessary for the particular domain, can be ignored leading to a simpler analysis that does not consider phenomena that are deemed to be outside of the scope of the domain.



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Second, there may also be a need for specialisation of some of the ontological constructs in order to enhance analysis of a grammar pertaining to a particular domain. For example, our analyses of Web services standards such as ebXML, BPEL4WS or BPML included the mapping of various activity types to the ontological construct transformation. Such findings could motivate the derivation of relevant sub-types of transformation when it comes to the context of business process management. Third, the derivation of a focused ontology will require adapting the terminology of the analysed domain for two reasons. On the one side, the terms of the ontology might not be intuitive (e.g., conceivable state space within the BWW ontology). On the other side, the analysed domain might have its own established terminology. An example is the area of workflow modelling techniques, in which the Workflow Management Coalition had a significant impact with its glossary.

The argument for a focused ontology might be quite convincing and even seen as trivial. However, the development of focused ontologies faces a major challenge. The decisions about deleting constructs, adding sub-types and renaming constructs have to be based on a substantial number of ontological analyses before they can be justified. Thus, such focused ontologies are not readily available. In general, current ontological analyses focus on the selection of an adequate ontology and the evaluation of modelling grammars against that ontology. Ontological weaknesses are often interpreted as a weakness of the ontology or a weakness of the analysed grammar. It might be however a weakness of the comparison as the ontology and the analysed grammar do not fit. This situation can be explained by the highly interdisciplinary

Business Systems Analysis With Ontologies

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