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Proceedings of the Information Systems Foundations Workshop

Ontology, Semiotics and Practice 1999

 

Enriching the Ontological Foundations of Modelling in Information Systems

Simon Milton† and Ed Kazmierczak‡

†School of Management Information Systems,  Deakin University
As of Jan 2000:  Dept of Information Systems, The University of Melbourne

s.milton@dis.unimelb.edu.au

 

‡Department of Computer Science and Software Engineering,

The University of Melbourne

ed@cs.mu.oz.au

Abstract

In this paper we consider the relationship between ontology and data models. The position argued in this paper is that Chisholm’s ontology has the potential to be a unifying theory for data models. In addition, our research has lead us to the position that ontologies founded in the philosophical tradition of realism seem to serve the purpose of a unifying framework for data models. Further, we have seen the realistic ontologies by Mario Bunge and Roderick Chisholm used in information systems. We believe that realistic ontologies have a role to play in understanding information systems and that a deeper comparison of Bunge’s ontology, which tends towards scientific realism, and Chisholm’s ontology, which is from common sense realism, may enrich the ontological foundations of Information Systems.

Keywords

IS Design, Database Design, Data Modelling, Process Design

Introduction

Modelling is important in much of human activity and can achieve certain important goals. Models allow us to understand the key aspects of an artefact, or a process before actually implementing it. Models also allow us to communicate a shared understanding of something. Further, models allow us to analyse properties of things such as systems, buildings, or structures with a view to understanding their key characteristics before going to the expense of implementing them. The analysis performed varies according to the style and capability of the model concerned and the subject of the analysis, that is, models let us investigate properties of interest of the subject.

In information systems modelling is vital because we wish to describe and analyse how people perceive some universe of discourse. The objective is to be able to implement a technological and social system that records and processes information from the universe of discourse. The modelling process allows us to describe and understand the proposed system and the model allows to share that understanding in a way that is meaningful for the people involved and that can be implemented using suitable technology. Ideally, the model should be a good predictor of the way that the final system will actually be used; in short, we would require that our model capture an accurate and consistent view of the reality with which the organisation is concerned.

Of course, data models feature prominently in Information Systems. We construct data models in order to understand significant entities in the universe of discourse, their relationships with other entities as well as properties possessed by each of the entities. Our interest is in finding a unifying framework based upon recognised theory which we can use to discuss and rationalise about data models. Now, there are numerous data models in the literature and, at least superficially, they appear to have some features in common. Any unifying framework will need to discuss the similar and different features between data models within a single set of concepts and terms. One possibility for providing such a framework comes from the philosophical study of ontology. We go one step further, and suggest that ontology is useful as a theory with which to theorise about data models. The reason for this is that ontology can describe what constitutes reality. The reality in which we are interested is that perceived by organisations, which are collections of people working towards a common goal.

Our position is that an ontology can provide a theory upon which to base a unifying framework for data models. We also argue that the ontology by Roderick Chisholm, which is a common sense realistic ontology, has the potential to enrich data models and our understanding of data models. Indeed, we have found that there is a good degree of overlap between the selected ontology and the modelling frameworks we studied.  Finally, we extrapolate this position and explore the potential of realistic ontologies in furthering our understanding of modelling in Information Systems.  We can assert this position by stating three propositions that we explore in the sequel.

Proposition 1: Ontology can provide a theory upon which to base a unifying framework for data models.

Proposition 2: Chisholm’s ontology can be used as a unifying framework in which to compare, contrast and investigate different data models.

Proposition 3: Realistic ontologies can help to further our understanding of modelling in Information Systems.

Recently, there has been considerable research utilising an ontology based upon one by Mario Bunge (Bunge, 1977, Bunge, 1979) to examine systems analysis and design methodologies (Wand and Weber, 1989, Wand and Weber, 1990, Wand and Weber, 1993, Weber, 1997, Wand, 1996, Rohde, 1995).  Some of this research has investigated data models (Wand et al., 1993, Weber and Zhang, 1996). We have conducted ontological studies of five representative data models using an ontology by Roderick Chisholm (Chisholm, 1992, Chisholm, 1996).

We have investigated Chisholm’s ontology as a potential unifying framework for data models, and found that it has a good degree of commonality between Chisholm’s ontology and a number of representative data models (Milton et al., 1998). In a similar way to Bunge’s ontology, Chisholm’s ontology also has the potential to act in more general information systems modelling because it handles static and dynamic aspects of ‘what there is’, and it recognises that situations and structures change over time. Realistic ontologies, of the style of Chisholm’s, allow for the perception of reality and abstract to a level that is appropriate for people and for these reasons can form the basis for modelling the application domain or reality (Wand et al., 1995).

Chisholm’s ontology is a commonsense realism that allows for human perceptions of reality and abstracts to a level more natural for people, and may be useful in explaining some information systems phenomena from the application domain. This is somewhat different from the ontology by Bunge (Wand, 1996). It would be interesting to compare and contrast Bunge’s ontology, an ontology tending towards scientific realism, with Chisholm’s commonsense realistic ontology.

Ontology as a Unifying Framework for data models

In this section we explore our first proposition:

Proposition 1: Ontology can provide a theory upon which to base a unifying framework for data models.

We begin by trying to understand ontology from a philosophical viewpoint, and then argue that each of the data models considered in our studies possesses ontological elements.  A good explanation of ontology can be found in (Honderich, 1995),

“Ontology, understood as a branch of metaphysics, is the science of being in general, embracing such issues as the nature of existence and the categorical structure of reality. … Different systems of ontology propose alternative categorical schemes. A categorical scheme typically exhibits a hierarchical structure, with ‘being’ or ‘entity’ as the topmost category, embracing everything that exists”.

A system of ontology provides us with a set of terms for discussing the nature of existence and the categories making up reality to which terms are related. Through its terms, an ontology can be used to create an abstraction from reality.

Data models also provide us with terms with which to build models of reality, for example, OMT uses terms including objects and associations, ER uses entities and relations and FDM uses entities of various sorts and functions. The terms used in a data model give its view of reality, that is, models of reality that are possible in a specific data model are composed of the terms provided by the data model. Data models, however, do not attempt to form taxonomies in which to describe reality, nor do they seek to embrace everything that exists. Data models provide us with a framework, which includes terms, for constructing models of reality.

Further, each term is given meaning through a concept. The world view implicit in a data model or an ontology is also expressed through the concepts that give meaning to terms. For example, ontologies and data models often discuss attributes. ‘Attribute’ has an associated concept in each of an ontology and a data model that reveals the specific meaning behind ‘attribute’ for the data model and the ontology. The concept that refers to the term ‘attribute’ may be different in each. A specific data model, may see attribute as applying to a ‘class of entities’ (more terms and concepts) or that each entity in a specific class must exhibit the same set of attributes.

In contrast, an ontology may see a specific attribute as being exemplified by many and varied ‘individuals’ (another term that is probably related to entity), and that different individuals may exemplify the same attribute simultaneously. Clearly, for the same term ‘attribute’, there is a degree of synonymity in the concept giving meaning to the term for the data model and ontology, but there is also a myriad of nuances that define ‘attribute’ for the data model and ontology. Examining all terms, the world view of ontologies and data models is contained in the totality of terms and concepts for each.

A Unifying Framework for Studying Data Models

In this section we explore the conjecture:

Proposition 2: Chisholm’s ontology can be used as a unifying framework in which to compare, contrast and investigate different data models.

We begin by discussing the commonsense realistic ontology proposed by Roderick Chisholm. It is difficult, if not impossible, to describe an ontology in a short space and so we concentrate on a few key aspects. The categories in Chisholm’s ontology are organised into a taxonomy that is shown in the figure 1 below. The theory proposed by Chisholm divides the world into entities that are ‘contingent’ and don’t have to exist, and ‘necessary’ entities that must exist in order for his theory to be consistent.  We concentrate on the boldface categories highlighted in the figure below as these are also typical of many systems.

Figure 1: Chisholm’s Categories

Chisholm’s ontology centres on individuals and the attributes they exemplify. Chisholm stresses that attributes are fundamental to his ontology. As we will see later, he reduces other terms by defining them using only attributes. The terms and associated concepts of ‘individual’ and ‘attribute’ have descriptions that show not only their individual disposition, but also their roles in sets, classes, and relations. We also describe these below. These terms are not fundamental to the ontology but nevertheless are important terms that are discussed and are appropriate for our goals.

Individuals are discernable and transient objects and need not be material (or physical) in nature. Examples of individuals are an accountant named Freda, the annual financial statements for Ericsson, and Orly International Airport.  Individuals are identified by using the attributes that only they exemplify. Further, individuals may have constituents thereby giving them structure. Constituents may be other individuals (called parts) or may be boundaries (the other constituents). For example, consider Orly Airport. It has several rent-a-car franchises, bars, restaurants, departure gates, each of these are parts of Orly Airport and are also individuals. In this example, most of these can be further sub-divided

Individuals may exemplify attributes. Orly Airport is very busy; Nokia’s balance sheet is good; Freda, our accountant, is of age 43. Some attributes may never be exemplified and others cannot be exemplified. For example, Orly Airport may never be green. We can be sure that Orly Airport can never be liquid. Chisholm also allows for compound attributes which may consist of other compound attributes or simple attributes. He suggests that an attribute may be the conjunction or disjunction of several attributes. For example, the attribute of ‘being good’ with respect to Nokia’s financial statements may be the conjunction of being in surplus (profit) and being of good credit rating.

In Chisholm’s ontology, attributes are used to restrict membership of sets and classes. Further, Chisholm reduces discussion of classes to discussion of attributes. Specifically, this is achieved by adopting Russell’s reduction of classes to attributes (Russell, 1908). This has the effect of building classes and sets from individuals through the exemplification of attributes and not by constructing elaborate class structures. For example, suppose we are maintaining a taxonomy of plants. Periodically, the taxonomy may change quite drastically without a change in the majority of attributes exhibited by the plants involved. Using Chisholm’s ontology classes can change radically through a change in membership criteria based on attribute exemplification.

Classes and sets can be selected based upon attributes that are conjunctions and disjunctions of other attributes, and in this sense complex class relationships can be realised. The central point remains, that individuals come together to form classes and are fundamental to the ontology

Relations may exist between individuals but relations, according to Chisholm, are unidirectional and not necessarily reciprocated. Further, relations are defined in terms of attributes by reducing relations to ordered pairs of attributes. For an ordered pair to represent unidirectional relations, attributes that uniquely describe each individual need to be found. For example, suppose that Freda (our accountant) is recruited to audit Nokia’s books then an attribute being an ordered pair of identifying attributes for Freda and Nokia would have to be exhibited by Freda.

A summary of theses key ideas is given in table 1 below and continued on the next page.

 

Concept

Part

Description

 

Individual

Core

Chisholm allows for discernible and transient objects. These are called individuals. Individuals come into being (are created) and pass away (destroyed). In this sense they are transient.

 

 

Identity

Each individual possesses an attribute (or several attributes) that uniquely identifies it.

 

 

Structure

Individuals may have constituents. These are either other individuals (known as parts) or boundaries (the other constituents.) Individuals that make up parts of others are still thought of as being individuals.

 

Attribute

Core

Attributes are exhibited by individuals. They are central to Chisholm’s ontology, after individuals. Further, attributes are enduring, in the sense that they don’t come into being and don’t pass away. Further, attributes must be loosely coupled with individuals.

 

 

Equivalence

Attributes can be equivalent in the sense that if something exhibits one attribute then it exhibits the other. This is called conceptual entailment.

 

 

Complexity

Attributes may be simple or complex. Complex attributes are combinations of either simple or other complex attributes. The mechanism suggested by Chisholm is one involving conjunction and disjunction of attributes. He feels there may be other ways of providing for this complexity.

Classification (classes and sets)

Core

Classes and sets are provided using attributes, in the ontology. Specifically, it is through the attributes that membership of classes is determined.

 

Relation

Core

Individuals may be related. Specifically, relations are attributes (an ordered pair). The ontology requires that attributes that identify the participating individuals are required. Further, that the relations are unidirectional (not bidirectional).

 

Table 1: Concepts for Statics in Chisholm’s Ontology

We have considered five data models in our investigations: the Entity Relationship Model (ER), the Functional Data Model (FDM), NIAM, the Semantic Data model (SDM) and OMT as it pertains to the Unified Modelling Language (UML). We have conducted a comparison between these data models and Chisholm’s ontology and have discovered that there is a good degree of fit. A summary of the findings is given in table 2 below. In the table we use a • to indicate support for a feature of Chisholm’s ontology, a •p to indicate qualified support for a feature in Chisholm’s ontology and a X to indicate no support for a feature in Chisholm’s ontology. The features chosen from Chisholm’s ontology are the key static features.

 

Ontological Concept

ER

FDM

SDM

NIAM

OMT

Individual

•p

•p

•

•p

•

Attribute

•p

•p

•p

•p

•p

Classification

•p

•

•p

•p

•p

Relation

•p

•

•

•p

•p

Table 2: Results of the Comparison of Selected Data
Modelling Frameworks Using Chisholm’s Ontology

The indicative results in Table 2 show a good degree of coverage for a number of key concepts from Chisholm’s ontology by all data models. However, each data model possesses some concepts for which there is only partial or qualified support in Chisholm’s ontology. Due to the constraints of space we can only give a brief summary of the results in this paper and the reader is referred to  (Milton et al., 1998) for more a detailed analysis.

Chisholm’s ontology views the world as a collection of individuals and relations between them, and the ontology uses attributes to describe both individuals and relations. Attributes are universals and endure, and, consequently they are loosely coupled with individuals. Attributes are also used to determine class and set membership. Our comparison suggests that this is to a large extent a similar world-view as those imparted by the data models and there is a good level of agreement with the ontology and the modelling frameworks that we have studied. On the other hand the data models lack the full generality of Chisholm’s ontology. The major departures from Chisholm are in the more subtle nature of relations and attributes and the implications of a tighter coupling between individuals and attributes in the data models; particularly as these pertain to sets and relations which are primitive in the data models.

Classification in the ontology is evident through the attributes exemplified by members of classes. In the ontology, classes are related to each other by the intersections and unions of the attributes used to select them.  They thereby can simulate class hierarchies. This approach is entirely different from most classification approaches used by data models and also different from the rich and rigid class hierarchies that are prevalent in some data models.

The consequence of these departures from the ontology is that it is likely one can model a narrower range of situations using the studied data modelling frameworks than Chisholm’s ontology, although this requires further investigation. Further, Chisholm’s ontology has the potential to change our view of data modelling by its increased flexibility achieved through bidirectional relations and through its loose-coupling of attributes with respect to individuals. In turn, this has positive implications for the flexibility of models which are subject to radical or ongoing change. It is the formation of classes through attributes as a direct consequence of loose coupling that is most beneficial for flexibility.

We have found that ER, OMT’s Object Model, and NIAM do not support such class flexibility. This is principally because of tight coupling between individuals and attributes found in ER, OMT’s Object Model, and by practice in NIAM.

We found that FDM captures the fundamental nature of Chisholm’s ontology more closely than the other modelling frameworks. Due to its evident simplicity, FDM has more potential to be able to support other elements presently not supported that are directly related to loose coupling of attributes and individuals and to classification. Its simplicity means that there are few, if any, concepts in FDM that are antinomous with respect to concepts from the ontology in either a contradictory or contrary manner.

We have also found SDM to be reasonably close to the ontology. SDM’s complexity with respect to its class system makes it a difficult modelling framework to use to fully express Chisholm’s ontology. Nevertheless, it would be interesting to investigate SDM further.

Concluding, we can see from the results that the modelling frameworks share, to a large degree, the world view of the ontology. Consequently there is good reason to believe that Chisholm’s ontology can serve as a unifying framework in which to explore these data models. The areas of departure tend to be of the nature of a difference in overlap with the modelling frameworks rather than complete absence of support. Also, all concepts had a high degree of emphasis with respect to their core. There are, however, some issues that need investigation. The area of most concern is that of classification. Clearly, the rigidity of class construction and the presence of rigid class hierarchies is not supported in the ontology. As implementation efficiencies these may be acceptable. As modelling features there appears to be little support in traditional realistic philosophy for such an approach.

Commonsense Realism: The Heritage of Chisholm’s Ontology

We have argued in the section above that there is a good degree of fit between Chisholm’s ontology and each of the data models. This raises the question of what is it about Chisholm’s ontology which resonates with the data models. The course that we take is to explore proposition three, which is,

Proposition 3 Realistic ontologies can help to further our understanding of modelling in Information Systems.

We do this by first exploring the heritage of Chisholm’s philosophy, which is commonsense realism, and then discuss what potential there is in commonsense realism for Information Systems.

Each ontology that one considers assumes definitions, and uses terms, that are steeped in the western philosophical tradition (Flew, 1989), and the attitude taken by the author of an ontology to certain key questions reveals his or her philosophical outlook. This outlook is also expressed in terms with deep philosophical meaning (Kim and Sosa, 1995, Audi, 1995, Honderich, 1995, Dancy and Sosa, 1992). For example, a philosopher may state in his or her ontology that they discount this or that aspect of Plato’s writing. Further, they may claim that their work is idealistic or, alternatively, realistic.

Chisholm’s philosophical stance is stated quite clearly in his book (Chisholm, 1996). Briefly, there are two aspects to the philosophy in Chisholm’s ontology. Firstly there is the brand of realism that he adopts which is a critical commonsense realism. Secondly, there is the epistemological stance that determines the nature of the knowledge that he envisages will coexist with his terms and categories. This latter aspect of Chisholm’s ontology is not explored further in this paper.

In this section we take a deeper look at realism and the variants within realistic philosophy.

Realism in any area of thought is the doctrine that certain entities allegedly associated with that area are indeed real. Common sense realism — sometimes called ‘realism’, without qualification — says that ordinary things like chairs and trees and people are real. Scientific realism says that theoretical points like electrons and fields of force and quarks are equally real. And psychological realism says mental states like pains and beliefs are real.   (Dancy and Sosa, 1992)

To elaborate this definition we may appeal to Dancy (Dancy and Sosa, 1992) for a clarification of commonsensism.

“Commonsensism is the view that we know, most, if not all, of those things which ordinary people think they know and that any satisfactory epistemological theory must be adequate to the fact that we do know such things.”

To understand critical commonsensism we can appeal to C.S. Pierce’s definition.

“In later writings his anti-Cartesianism took the form of ‘critical commonsensism’: our inquiries are guided by a slowly evolving body of vague commonsense certainties which are, in principle, fallible; rational self-control requires that we try to doubt these in order to establish that they genuinely form part of commonsense.” (Dancy and Sosa, 1992)

Critical commonsensism differs from commonsensism in that it demands a more rigorous standard of support for knowledge to be acquired. Hence the term ‘critical’. Chisholm’s ontology is an example of critical commonsensism. Nevertheless his ontology is also one of “extreme realism” (Chisholm, 1996).

Now, the only other major ontology recognised in Information Systems is the realistic ontology by Bunge, although we may say that the realism of Bunge’s ontology tends towards scientific realism. Thus, the ontology we selected for our study and Bunge’s ontology are both realistic. Is there something about realistic ontologies which are particularly suited to modelling?

We have already asserted that an ontology provides terms and concepts for understanding what there is and that the world view of ontologies and data models is contained in the totality of the terms and concepts for each. The two key terms of Chisholm’s ontology are individual and attribute, that is, that individuals exist in reality and that these individuals can be described by the attributes that they possess. Attributes and individuals form the realistic core of Chisholm’s ontology and both terms are present in related realistic ontologies. In the section above we have argued that most data models possess concepts which overlap with these two fundamental terms and so it may be conjectured that data models tend to Chisholm’s brand of realism.

Next, let us explore this position in the broader context of understanding information systems. To do this we will need to have the capacity for analysing processes, for example, in order to understand the process of organisational change or an organisation’s business processes. Chisholm’s ontology has the capacity to model state, changes in state, and processes. It does this through the related categories of event and state. Further, Chisholm’s ontology allows for enduring events that others may call processes. For example, the economy of France may, at one time, be unhealthy. With that state of affairs there may be several attributes that are exemplified such as being high unemployment and being negative ‘growth’ in gross domestic product (GDP). France may undergo a process of reform, an enduring event, that may result in France instead exemplifying the attributes of being low unemployment and positive growth in GDP.

There are two points to make here. Firstly, we believe that there is potential in this aspect of Chisholm’s ontology for understanding processes, however, we haven’t specifically studied this aspect of the ontology in great detail and it will require further investigation. Secondly, we have not yet fully investigated the heritage of the terms state and event and so cannot say for certain that they figure prominently in realistic ontologies. What we can do, however, is to be pragmatic in our selection of ontology and to ensure that it meets the criteria for our purpose. In the case of our investigations into data models we have chosen Chisholm’s ontology on the basis of the following four criteria, one of which is the requirement that the selected ontology be able to describe the dynamics and statics of ‘what there is’.

1. If the ontology is to become a unifying framework, then it must discuss elements of a similar nature to data modelling frameworks. This is important because data modelling frameworks contain an array of elements that are used to construct models of reality.

2. The ontology should be set at a level which generalises from the linguistic expression of ‘what there is’. Specifically, many ontologies are conceived by people expressing, through language, their understanding of ‘what there is’. We desire an ontology that has abstracted away from these expressions and generalises in appropriate ways. Appropriateness in this context means that the generalities used in the ontology help directly in evaluating and discussing data modelling frameworks. In some ways this is a restatement of the first criteria but from the viewpoint of ontology.

3. The ontology must deal with statics and dynamics. Specifically, reality changes, and so a description of reality changes. A description of reality in which we are interested is one that adheres to an ontology. This in turn requires that the ontology according to which the description is fashioned, must deal with change. Specifically, the ontology should handle both static and dynamic aspects of reality. A large number of ontologies deal with either dynamics or with statics. Fewer ontologies deal with both.

4.  The ontology must allow for the construction of multiple simultaneous descriptions of reality, because, an organisation has many groups that view reality in different ways.

We are not sure that the dynamics of Chisholm’s ontology are realistic, but there is a realistic core to the statics of the ontology.

Discussion and Future Research

In this position paper, we have argued, based upon our research, that Chisholm’s ontology has the potential to be a unifying framework for data models. Together with related research we observe that two realistic ontologies have now been applied to information systems in a role as theory either of a predictive or unifying nature. The two ontologies, by Mario Bunge and Roderick Chisholm, represent different styles of realism. Bunge’s ontology is one of realism tending towards scientific realism whereas in contrast, Chisholm’s ontology is one of commonsense realism. We believe that on the basis of these bodies of research that realistic ontologies have a significant role to play in theorising about information systems.

We conjecture that Chisholm’s ontology will be useful in modelling phenomena that are related to the application domain in which social or human issues dominate, because it is one of commonsense realism. In contrast, that Bunge’s ontology will prove better adapted to the implementation environment or to the application domain when human or social issues are absent.

The most critical area for further research is to investigate the nature of the relationship between Chisholm’s ontology and Bunge’s ontology. We also need to deepen our understanding of the ramifications of Chisholm’s ontology, particularly concerning dynamics.

We are not overly concerned about finding a theoretical basis upon which to establish the field or discipline of information systems.  Instead we note that ontology appears to be quite useful in rationalising about information systems development and implementation on the one hand, and in rationalising about information modelling and representation tools on the other. We have also argued that the more human-centred nature of Chisholm’s ontology together with the precise and, at times, fine-grained nature of Bunge’s ontology may prove to be an important combination in improving the process of information systems analysis and development.

There is no doubt that with any theory, experience applying it in practical situations is critical to its ultimate evaluation and to the determination of its effectiveness. This has been done to an extent with Bunge’s ontology. We hope that in addition to investigating the relationship between the two ontologies, we will also begin to evaluate some of our findings in practical situations.

Although we’ve not studied the relationship in any great detail, at this distance we feel that the statics of Chisholm’s ontology and Bunge’s ontology share a good level of commonality. The main differences between the ontologies lies in the degree of abstraction in the dynamics and to a lesser degree the statics of Chisholm’s ontology. The fine-grained extremely tightly defined nature of Bunge’s ontology contrasts with the coarser grained, less tightly defined, and more human centred focus of Chisholm’s ontology. We believe that there is potential for a powerful partnership between Bunge’s ontology and Chisholm’s ontology.  We also feel that Chisholm’s ontology may be able to complement Bunge’s ontology with respect to the human-centred aspects of modelling in information systems development and implementation while still enabling us to capitalise on the formalised rigour of Bunge’s ontology. Much further research is required to more clearly define the nature, scope, and feasibility of the relationship between Bunge’s ontology and Chisholm’s ontology.

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Simon Milton and Ed Kazmierczak (c) 1999. The authors assign to the IS Foundations Workshop held at the Department of Computing, Macquarie University on Wednesday 29 September 1999,  and to educational and non-profit institutions, a non-exclusive licence to use this document for personal use and in courses of instruction provided that the article is used in full and this copyright statement is reproduced.  The authors also grant a non-exclusive licence to the IS Foundations Workshop to publish this document in full in the Workshop Proceedings. Those documents may be published on the World Wide Web, CD-ROM, in printed form, and on mirror sites on the World Wide Web. Any other usage is prohibited without the express permission of the authors.

 

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