=Paper=
{{Paper
|id=Vol-365/paper-5
|storemode=property
|title=An Ontological Framework of Method Engineering: An Overall Structure
|pdfUrl=https://ceur-ws.org/Vol-365/paper5.pdf
|volume=Vol-365
|dblpUrl=https://dblp.org/rec/conf/emmsad/Leppanen07
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==An Ontological Framework of Method Engineering: An Overall Structure==
https://ceur-ws.org/Vol-365/paper5.pdf
An Ontological Framework of Method Engineering: An
Overall Structure
Mauri Leppänen
Department of Computer Science and Information Systems
P.O. Box 35 (Agora), FI-40014 University of Jyväskylä, Finland
mauri@cs.jyu.fi
Abstract. A large number of strategies, approaches, meta models, techniques
and procedures have been suggested to support method engineering (ME). Most
of these artifacts, here called the ME artifacts, have been constructed, in an
inductive manner, synthesizing ME practice and existing ISD methods without
any theory-driven conceptual foundation. Also those ME artifacts which have
some conceptual groundwork have been anchored on foundations that only
partly cover ME. This paper presents an ontological framework, called
OntoFrame, which can be used as a coherent conceptual foundation for the
construction, analysis and comparison of ME artifacts. Due to its largeness, we
describe here its modular structure composed of multiple ontologies. For each
ontology, we highlight its purpose, sub-domains and theoretical foundations.
We also mention the approaches and process by which OntoFrame has been
constructed.
1 Introduction
Engineering of an information systems development (ISD) method is far from trivial
in practice. In the first place, ISD methods are abstract things with divergent semantic
and pragmatic meanings. The former implies that conceptions of what ISD methods
should contain may vary substantially [11, 15, 25, 26, 30, 40]. The latter suggests that
views of roles, both technical and political, that ISD methods play in ISD may be
quite different [11, 72]. Existing methods also differ from one another in their
fundamental assumptions and approaches [11, 30]. Second, it is often difficult to
characterize the target ISD situation in a way which makes it possible to conduct a
proper selection of and suitable adaptation in existing methods for an organization or
a project. Third, it is frequently unclear which kind of strategies (i.e. from “scratch”,
integration, adaptation) and processes should be applied in each stage of the
engineering of an ISD method. Fourth, most of the method engineering (ME)
situations suffer from the lack of time and other resources, causing demands for
carrying out ME actions in a more straightforward and effective manner.
A large array of ME strategies and approaches (e.g. [36, 58, 60]), meta models
(e.g. [15, 21, 25, 32, 54]), ME techniques (e.g. [7, 33, 39, 57, 62]) and ME procedures
(e.g. [21, 31]) have been suggested to support method engineering. These ME
artifacts, as we call them here, sustain, however, several kinds of shortcomings and
deficiencies [46]. One of the major limitations in them is the lack of a uniform and
consistent conceptual foundation. Most of the ME artifacts have been derived, in an
inductive manner, from ME practice and existing ISD methods without any theory-
based conceptual ground. Also those ME artifacts that have a well-defined
underpinning have been anchored on foundations that only partly cover the ME
domain.
ME is a very multifaceted domain. It concerns not only ME activities, ME
deliverables, ME tools, ME actors and organizational units, but, through its main
outcome, an ISD method, also ISD and more specifically ISD activities, ISD
deliverables, ISD actors, ISD tools, etc. Furthermore, ME involves indirectly, through
information system (IS) models and their implementations, IS contexts as well as
those contexts that utilize information services provided by the IS. Thus, in
constructing an ME artifact it is necessary to anchor it on a coherent conceptualization
that covers ME, ISD and IS, as well as the ISD and ME methods. In ontology
engineering literature (e.g. [17]) a specification of the conceptualization of a domain
is commonly called an ontology. Hence, what we need here is a coherent set of
ontologies which cover all the aforementioned sub-domains of ME.
The purpose of this paper is to suggest an ontological framework, called
OntoFrame, that serves as a coherent conceptual foundation for the construction,
analysis and comparison of ME artifacts. OntoFrame is composed of multiple
ontologies that together embrace all the sub-domains of ME. It has been constructed
by searching for “universal” theoretic constructs in the literature (the deductive
approach), by analyzing existing frameworks, reference models and meta models (the
inductive approach), and by deriving more specific ontologies from generic ontologies
above them in the framework (the top-down approach [70]). The construction has
been directed by the goals stated in terms of extensiveness, modularity, consistency,
coherence, clarity, naturalness, generativeness, theory basis and applicability. The
ontological framework is quite large, comprising 16 individual ontologies [40]. Here,
we are only able to describe its overall structure and outline the ontologies on a
general level (Section 2). We also discuss the theoretical background and approaches
followed in engineering it (Section 3). The paper ends with the discussion and
conclusions (Section 4).
2. An Overall Structure of OntoFrame
A conceptual framework is a kind of intellectual structure which helps us determine
which phenomena are meaningful and which are not. OntoFrame, as an ontological
framework, aims to provide concepts and constructs by which we can conceive,
understand, structure and represent relevant phenomena pertaining to method
engineering. Deriving from two disciplines, ontology engineering (e.g. [13, 18]) and
conceptual modeling (e.g. [9, 34]), OntoFrame has been set the following goals. To
advance communication between people, the framework should be clear and natural.
To balance different needs for specificity and generality, OntoFrame should be
composed of ontologies that are located at different levels of generality. To build on
some more stable and solid ground, the main building blocks of OntoFrame should be
driven from relevant theories. To ease making extensions and still maintaining
coherence and consistence, OntoFrame should be modular and generative. To cover
the relevant phenomena of ME, OntoFrame should also be extensive. And last but not
least, OntoFrame should be applicable in the construction, analysis and comparison of
ME artifacts.
The ontological framework is composed of four main parts (Figure 1). The first
main part, the core ontology, provides basic concepts and constructs to conceive and
structure human conceptions of reality and use of a language in general. The second
main part, the contextual ontologies, focuses on conceptualization of information
processing as contexts or parts thereof, on certain processing layer, from certain
perspective, and as being modeled on some model level. The third main part, the
layer-specific ontologies, has been specialized from those above in the framework to
cover the sub-domains of IS, ISD and ME with more special concepts and constructs.
The fourth main part, the method ontologies, conceptualizes the nature, structure and
contents of the ISD method and the ME method. In the following, we describe each of
these main parts and their ontologies in terms of purposes, sub-domains, and
theoretical foundations.
The purpose of the core ontology is to provide the basic concepts and constructs
for conceiving, understanding, structuring and representing fundamentals of reality. It
comprises seven ontologies: generic ontology, semiotic ontology, intension/extension
ontology, language ontology, state transition ontology, abstraction ontology, and UoD
ontology. Each of these ontologies has the scope, purpose and role of its own in the
core ontology. The generic ontology, founded on the constructivist position [9],
provides the most generic concepts from which all the other concepts have been
derived by specialization. It is the top ontology [19] in our framework. The most
elementary concept is ‘thing’, meaning any phenomenon in the “objective” or
subjective reality. The semiotic ontology defines concepts that are needed to
recognize the semiotic roles of and relationships between the things. The main
concepts, adopted from semiotics [53], are ‘concept’, ‘sign’, and ‘referent’. The
intension / extension ontology serves as a conceptual mechanism to specialize the
notion of concept and defines its semantic meaning [22]. The notions of intension and
extension enable to differentiate between ‘basic concept’, ‘derived concept’, ‘abstract
concept’, ‘concrete concept’, ‘instance concept’ and ‘type concept’.
The language ontology provides concepts for defining the syntax and semantics of
a language. Based on linguistics (e.g. [50]), it defines concepts such as ‘language’,
‘alphabet’, ‘symbol’, and ‘expression’. The state transition ontology is composed of
concepts and constructs for the recognition of dynamic phenomena in reality in terms
of states, state transitions, and events. The abstraction ontology serves concepts and
constructs for abstraction by classification, generalization, aggregation, and grouping.
Deriving from the intension/extension ontology, it also distinguishes between the first
order abstraction and the second order abstraction (or the predicate abstraction). It is
based on the philosophy of science and abstraction theories by e.g. [14], [22], [51]
and [52]. The UoD (Universe of Discourse) ontology is composed of consolidated
concepts through which reality can be conceived from a selected viewpoint. These
concepts are ‘UoD state’, ‘UoD behavior’ and ‘UoD evolution’.
The contextual ontologies help us recognize, understand, structure and represent
phenomena in reality (a) as some contexts or parts of contexts, (b) on some processing
Core ontology:
OntoFrame - Generic ontology
- Semiotic ontology
- Intension/extension
ontology
- Language ontology
- State transition ontology
- Abstraction ontology
- UoD ontology
Contextual
ontologies Context
ontology
Layer Model level
ontology ontology
Perspective
ontology
RW process
Layer-
specific
IS ontology
ontologies Method
ontologies
ISD method
ISD ontology
ontology
ME method
ME ontology
ontology
Fig. 1. Overall structure of OntoFrame
layers, (c) from some perspectives, and (d) as being modeled on some model
levels. These ontologies, orthogonal to one another, are: context ontology,
layer ontology, perspective ontology, and model level ontology. The context
ontology defines seven related contextual domains, called the purpose domain, the
actor domain, the action domain, the object domain, the facility domain, the location
domain, and the time domain. For each domain, the most essential concepts and
constructs are provided. The ontology is rooted in case grammar [10], pragmatics
[48], and activity theory [8]. The layer ontology helps us structure and relate, on a
general level, phenomena of information processing and its development at three
layers, namely information systems, information systems development and method
engineering. This ontology is based on systems theory [49] and information systems
science (e.g. [9, 12, 26]). The perspective ontology provides a set of well-defined
perspectives to focus and structure the conceptions of contextual phenomena. The
perspectives are: systelogical, infological, conceptual, datalogical, and physical
perspectives. The perspective ontology is based on systems theory [49], semiotics
[55], and some seminal works (e.g. [27, 28, 73]). With the model level ontology, one
is able to create, specify and present models about reality in different modes. The
kernel of this ontology is a hierarchy composed of instance models, type models, meta
models, and meta meta models. The ontology is based on works such as [6], [9] and
[27], many of which have their roots in linguistics and philosophy of science.
The third main part of OntoFrame is called the layer-specific ontologies. While the
layer ontology gives the basic structures to distinguish between and relating the
information processing layers, the layer-specific ontologies elaborate the
conceptualizations of IS, ISD and ME. These ontologies are the IS ontology, the ISD
ontology and the ME ontology, correspondingly. Each of them is specified through
the concepts and constructs of contextual domains and perspectives. The IS ontology
helps us conceive, understand, structure, and represent phenomena in the IS, its object
system and utilizing system. It has been derived from the context ontology, and by
integrating constructs from multiple works (e.g. [1, 2, 24, 38, 49, 64, 68, 67]). The
ISD ontology provides concepts for conceptualization of contextual phenomena in
ISD. Besides deriving from more generic ontologies in the framework, it has been
built by selecting, abstracting, modifying and integrating concepts from a large array
of IS and ISD literature (e.g. [25, 35, 56, 59, 68]). Respectively, the ME ontology
covers contextual phenomena in method engineering. The ontology has been built on
works such as [3], [15], [20], [21], [36], [58], [61] and [69].
The fourth main part of the framework is the method ontologies, comprising the
ISD method ontology and the ME method ontology. The ontologies provide concepts
and constructs to conceive, understand, structure, and represent the nature, structure
and contents of the methods. The contents of the methods are conceptualized by the
ISD ontology and the ME ontology, correspondingly. The method ontologies have
been structured in accordance with the semantic ladder [67] and derived from a
number of frameworks of ISD methods [11, 15, 21, 25, 26, 30, 69].
To summarize, OntoFrame provides a holistic view of the sub-domains involved in
ME. The rationale behind the composition of OntoFrame from ontologies is based on
modularity, contextuality and generativeness. Modularity and generativeness help one
select an appropriate level of specificity on which phenomena in reality are to be
conceived. Contextuality facilitates the use of concepts and constructs for capturing
deeper meanings of single phenomena through relating them to other phenomena in
the context(s). Generating more specific concepts from generic concepts by
specialization advances the coherence and consistence of the framework.
3. The Approaches and Process of Engineering OntoFrame
Ontology engineering means categorizing, naming and relating phenomena in reality
in an explicit way. There are two sources of ontological categories [66]: observation
and reasoning. Observation provides knowledge of the physical world, and reasoning
makes sense of observation by generating a framework of abstraction. OntoFrame is
based on the extensive reasoning from the large literature on universal theories such
as semiotics, linguistics and systems theory, and works related more specifically to IS,
ISD and ME.
There are two approaches to deriving from the literature in ontology engineering.
In the inductive approach, source material is collected from single instance-level
artifacts (e.g. ontologies, frameworks, and methods) to abstract a more generic one. In
the deductive approach some universal-like theoretic constructs are first selected and
then deployed as an underlying groundwork for an ontology. We applied both of these
approaches. First, in building the core ontology we made a thorough analysis of
generic frameworks and ontologies (e.g. [4, 5, 9, 66, 71]) and derived the ontology
from them by selection, integration, and customization. In contrast, in engineering the
context ontology we first searched for disciplines and theories that address meanings
in sentence contexts [10], conversation contexts [48] and action contexts [8], and
derived the fundamental categorization of concepts into seven contextual domains.
After that we enriched the contents and structure of each domain with constructs from
existing artifacts. The fundamental structures in the perspective ontology, the model
level ontology and the layer ontology were also inferred from the relevant theories
(e.g. systems theory, semiotics, linguistics). For the rest of OntoFrame we mostly
applied the deductive approach to generate lower-level ontologies from higher-level
ontologies. In this process the existing literature was heavily utilized to complete and
customize the derived concepts and constructs for the concerned sub-domains.
Many of the conceptual frameworks in the ISD literature have been constructed
applying the inductive approach (e.g. [21, 63, 65]). Harmsen [21], for instance, built
his MDM model (Methodology Data Model) by deriving from existing classifications
and frameworks, resulting in a large set of IS-specific and ISD-specific concepts that
were justified through their source artifacts. A drawback of this kind of approach is
that it does not encourage bringing forward novel insights. In contrast, the BWW
model [71] has been anchored in Bunge’s ontology [4]. Through this deductive
approach the model pursues “universality” of concepts and constructs. In the
approach such as this there is, however, a risk that the theories originally crafted for
different domains may not cover the whole range of phenomena in the concerned
domain. We have tried to overcome these problems and risks by applying both of the
approaches. Theory-based constructs provided an underpinning that was tested,
enhanced and elaborated by the inductive derivation from current artifacts. The use of
the theories advanced not only the soundness of the framework but also innovations.
Another way to characterize our process of engineering OntoFrame is to use the
categorization of the approaches into top-down, bottom-up and mixed approaches
[70]. Our process mainly proceeded in a top-down manner through the following
stages: (1) building the core ontology, (2) deriving the contextual ontologies, (3)
establishing the layer-specific ontologies, and (4) deriving the method ontologies (see
Figure 1). From the main strategies available for ontology engineering we applied the
integration strategy whenever possible. In this way we could import existing
knowledge from those sub-fields in which views and concepts are relatively stable
and fit our main premises. Adaptation was carried out when needed.
For each of the ontologies in the framework we applied, in an iterative manner, an
ontology engineering procedure with the following steps (cf. [70]): (a) determine the
purpose and domain of an ontology, (b) consider reusing existing artifacts, (c)
conceptualize, (d) formalize (i.e. present in a graphical model), (e) evaluate, and (f)
document.
The ontological framework is aimed at a means of communication between human
beings, not for the use of computers. It comprises a large set of concepts and
constructs, and most of them are highly abstract. For these reasons, it is important to
present the framework in a concise yet understandable form. We deploy two
presentation forms: informal and formal (cf. [70]). The concepts are defined in a
vocabulary presented in a natural language. In addition, each of the ontologies is
expressed in a graphical model. From a large set of semi-formal languages we
selected a graphical language, and preferred the UML language to special ontology
representation languages (e.g. CLEO, LINGO, DAML+OIL and OWL) because of its
large and rapidly expanding user community, intrinsic mechanism for defining
extensions, and largely available computer-support.
4. Discussion and Conclusions
In this paper we have described the overall structure of the large ontological
framework called Ontoframe which serves as a conceptual foundation for the
construction, analysis and comparison of ME artifacts. We have also brought out the
purposes, sub-domains and theoretical bases of the ontologies contained in
OntoFrame, as well as the approaches and process by which OntoFrame has been
constructed.
As far as we know [40], there is no other presentation that would cover such a
large spectrum of sub-domains, on such a detailed level, as OntoFrame does. We have
intentionally aspired after this kind of holistic view in order to avoid the
fragmentation of views and conceptions that is typical of most of the research in our
field. The holistic view enables the recognition, comparison and integration of current
artifacts that have been built upon more limited foundations and views. Only some of
the existing representations (e.g. [9, 16, 28, 29, 71]) have been explicitly founded on
some theories.
OntoFrame is of benefit to both research and practice. With the ontologies
contained in OntoFrame it is possible to achieve a better understanding of the
contextual phenomena in IS, ISD and ME. OntoFrame provides a reference
background for scientists and professionals, thus enabling them to express themselves
about matters in the concerned sub-domains in a structured way, and based on that, to
analyze, compare and construct ME artifacts. The comprehensive and unified
framework establishes bridges between various approaches, disciplines and views
across three decades. OntoFrame also provides teachers and students with a large
collection of models of sub-domains and a comprehensive vocabulary with clear
definitions. OntoFrame is very large. It is not our intention that the whole range of
ontologies is applied in every case. Contrary to that, relevant parts of OntoFrame
should be selected depending on the problem at hand.
Assessment of a large ontological framework such as OntoFrame is difficult. Due
to the space limit, we discuss it here on a general level in terms of the goals set up for
OntoFrame. Coherence, generativeness, modularity, and balance between specificity
and generality of OntoFrame has been basically advanced by applying the top-down
approach by which concepts and constructs of ontologies on lower levels have been
derived by specialization from those on higher levels. Extensiveness has been aspired
at by anchoring the skeleton of the framework in universal theories. Achievement of
coherence and consistency has been aided by cross-checking the definitions in the
vocabulary and using graphical models to represent the ontologies. Clarity,
naturalness and applicability are goals that should be validated, preferably through
empirical tests.
Validation of applicability should, in the first place, involve each of the ontologies
in the framework individually. We have responded to this demand with the discussion
of validity of individual ontologies in separate articles (i.e. the context ontology [44],
the ISD ontology [43, 46], the abstraction ontology [45], the perspective ontology
[47] and the model level ontology [41]). Second, validation should concern the
framework as the whole. Some work for that has also been done. The applicability of
OntoFrame has been demonstrated in the analysis of ME artifacts in [42]. In [40] we
have deployed OntoFrame to construct methodical support for ME in the form of a
methodical skeleton. Although some evidence of the applicability of individual
ontologies, as well as of the whole framework has been got, more experience from the
use of OntoFrame in different kinds of ME contexts is definitely needed.
References
1. Allen, J. 1984. Towards a general theory of action and time. Artificial Intelligence 23(2),
123-154.
2. Borgo, S., Guarino, N., Masolo C. 1996. Towards an ontological theory of physical
objects. In Proc. of IMACS-IEEE/SMC Conference on Computational Engineering in
Systems Applications (CESA’96), Symposium of Modelling, Analysis and Simulation, Lille,
France, 535-540.
3. Brinkkemper, S. 1990. Formalization of information systems modeling. Dissertation
Thesis, University of Nijmegen, Amsterdam: Thesis Publishers.
4. Bunge, M. 1977. Treatise on basic philosophy, Vol. 3: Ontology I: The furniture of the
world. Dortrecht: D. Reidel Publishing Company.
5. Chisholm, R. 1996. A realistic theory of categories – an essay on ontology. 1st edition,
Cambridge: Cambridge University Press.
6. Dietz, J. 1987. Modelling and specification of information systems (In Dutch: Modelleren
en specificeren van informatiesystemen). Dissertation Thesis, Technical University of
Eindhove, The Netherlands.
7. Dominguez, E., Zapata, M.A. 2007. Noesis: Towards a situational method engineering
technique. Information Systems 32, 181-222.
8. Engeström, Y. 1987. Learning by expanding: an activity theoretical approach to
developmental research. Helsinki: Orienta-Konsultit.
9. Falkenberg, E:, Hesse, W., Lindgreen, P., Nilsson, B., Oei, J. L. H., Rolland, C., Stamper,
R., van Asche, F., Verrijn-Stuart, A., Voss, K. 1998. A framework of information system
concepts, The FRISCO Report (Web edition), IFIP.
10. Fillmore, C. 1968. The case for case. In E. Bach & R. T. Harms (Eds.) Universals in
Linguistic Theory. New York: Holt, Rinehart and Winston, 1-88.
11. Fitzgerald, B., Russo, N. & Stolterman, E. 2002. Information systems development –
methods in action. London: McGraw Hill.
12. Gasser, L. 1986. The integration of computing and routine work. ACM Trans. on Office
Information Systems 4(3), 205-225.
13. Gomez-Perez, A., Fernandez-Lopez, O. 2004. Ontological engineering. London: Springer.
14. Goldstein, R., Storey, V. 1999. Data abstraction: Why and how? Data & Knowledge
Engineering 29(3), 293-311.
15. Graham, I., Henderson-Sellers, B., Younessi, H. 1997. The OPEN process specification.
Reading: Addison-Wesley.
16. Grosz, G., Rolland, C., Schwer, S., Souveyet, C., Plihon, V., Si-Said, S., Achour, C.,
Gnaho, C. 1997. Modelling and engineering the requirements engineering process: an
overview of the NATURE approach. Requirements Engineering 2(2), 115-131.
17. Gruber, T. 1993. A translation approach to portable ontology specification. Knowledge
Acquisition 5(2), 119-220.
18. Gruber, T. 1995. Towards principles for the design of ontologies used for knowledge
sharing. International Journal of Human-Computer Studies 43(5/6), 907-928.
19. Guarino, N. 1998. Formal ontology and information systems. In N. Guarino (Ed.) Proc. of
Conf. on Formal Ontology in Information Systems (FOIS'98). IOS Press, 3-15.
20. Gupta, D., Prakash, N. 2001. Engineering methods from method requirements
specifications. Requirements Engineering 6(3), 135-160.
21. Harmsen, F. 1997. Situational method engineering. Dissertation Thesis, University of
Twente, Moret Ernst & Young Management Consultants, The Netherlands.
22. Hautamäki, A. 1986. Points of views and their logical analysis. Helsinki: Acta
Philosophica Fennica, Vol. 41.
23. Henderson-Sellers, B., Barbier, F. 1999. What is this thing called aggregation? In R.
Mitchell, A. C. Wills, J. Bosch & B. MeyerProc. (Eds.) Proc. of TOOLS EUROPE’99.
Silver Spring, MD: IEEE Computer Society Press, 236-250.
24. Herbst, H. 1995. A meta-model for business rules in systems analysis. In J. Iivari, K.
Lyytinen & M. Rossi (Eds.) Advanced Information Systems Engineering. LNCS 932,
Berlin: Springer, 186-199.
25. Heym, M., Österle, H. 1992. A reference model for information systems development. In
K. Kendall, K. Lyytinen & J. DeGross (Eds.) Proc. of Conf. on the Impacts on Computer
Supported Technologies on Information Systems Development. Amsterdam: Elsevier
Science, 215-240.
26. Hirschheim, R., Klein, H., Lyytinen, K. 1995. Information systems development –
conceptual and philosophical foundations. Cambridge: Cambridge University Press.
27. Hofstede ter, A.,Verhoef, T. 1997. On the feasibility of situational method engineering.
Information Systems 22(6/7), 401-422.
28. Iivari, J. 1989. Levels of abstraction as a conceptual framework for an information system.
In E. Falkenberg & P. Lindgren (Eds.) Information System Concepts: An In-Depth
Analysis. Amsterdam: Elsevier Science, 323-352.
29. Iivari, J. 1990. Hierarchical spiral model for information system and software
development. Part 2: Design process. Information and Software Technology 32(7), 450-
458.
30. Iivari, J., Hirschheim, R., Klein, H. 2001. A dynamic framework for classifying
information systems development methodologies and approaches. Journal of Management
Information Systems 17(3), 179-218.
31. Karlsson, F,. Ågerfalk, P. 2004. Method configuration: adapting to situational
characteristics while creating reusable assets. Information and Software Technology 46(9),
619-633.
32. Kelly, S., Lyytinen, K., Rossi, M. 1996. MetaEdit+: a fully configurable multi-user and
multi-tool CASE and CAME environment. In Y. Vassiliou & J. Mylopoulos (Eds.) Proc.
of the 8th Conf. on Advanced Information Systems Engineering (CAiSE’96). Berlin:
Springer, 1-21.
33. Kinnunen, K., Leppänen, M. 1996. O/A matrix and a technique for methodology
engineering. Journal of Systems and Software 33(2), 141-152.
34. Krogstie, J. 1995. Conceptual modeling for computerized information systems support in
organizations. Dissertation Thesis, NTH, University of Trondheim, Norway.
35. Kruchten, P. 2000. The Rational Unified Process: An introduction. Reading: Addison-
Wesley.
36. Kumar, K., Welke, R. 1992. Methodology engineering: a proposal for situation specific
methodology construction. In W. Kottermann & J. Senn (Eds.) Challenges and Strategies
for Research in Systems Development. Chichester: John Wiley & Sons, 257-269.
37. Langefors, B., Sundgren, B. 1975. Information systems architecture. New York: Petrocelli.
38. Lee, R. 1983. Epistemological aspects of knowledge-based decision support systems. In H.
Sol (Ed.) Proc. of Int. Conf. on Processes and Tools for Decision Support Systems.
Amsterdam: Elsevier Science, 25-36.
39. Leppänen, M. 2000. Toward a method engineering (ME) method with an emphasis on the
consistency of ISD methods. In E. Siau K (Ed.) Proc. of the Fifth Workshop on Evaluation
of Modeling Methods in Systems Analysis and Design (EMMSAD’00), Stockholm:
Sweden.
40. Leppänen, M. 2005. An Ontological Framework and a Methodical Skeleton for Method
Engineering, Dissertation Thesis. Jyväskylä Studies in Computing 52, University of
Jyväskylä, Finland.
41. Leppänen, M. 2006. An Integrated Framework for Meta Modeling. In
Manolopoulos Y., Pokomy J., Sellis T. (Eds.) Proc. of Conf. on Advances in Databases
and Information Systems (ADBIS’2006), LNCS 4152, Springer-Verlag, 141-154.
42. Leppänen, M. 2006. Conceptual evaluation of methods for engineering situational ISD
methods. Software Process: Improvement and Practice 11(5), 539-555.
43. Leppänen, M. 2007. A Contextual Method Integration. In Proc. of 15th Conf. on
Information Systems Development – New Methods and Practices for the Networked
Society (ISD 2006), Berlin: Springer-Verlag (in print).
44. Leppänen, M. 2007 A Context-Based Enterprise Ontology. In: Abramowicz W. (ed.) Proc.
of 10th International Conference on Business Information Systems (BIS 2007), LNCS
4439, Berlin: Springer, 273-286
45. Leppänen M. 2007. Towards an Abstraction Ontology. In Duzi M., Jaakkola H., Kiyoki
Y., Kangassalo H. (Eds.) Information Modelling and Knowledge Bases XVIII, Amsterdam:
IOS Press, 166-185.
46. Leppänen, M. 2007. Towards an Ontology for Information Systems Development – A
Contextual Approach. In Siau K. (Ed.) Contemporary Issues in Database Design and
Information Systems Development, Idea Group Inc. (in print).
47. Leppänen, M. 2007. A perspective ontology and IS perspectives. In Proc. of The 17th
European–Japanese Conference on Information Modelling and Knowledge Bases (EJC
2007), to be printed by IOS.
48. Levinson, S. 1983. Pragmatics. London: Cambridge University Press.
49. Mesarovic, M., Macko, D., Takahara, Y. 1970. Theory of hierarchical, multilevel, systems.
New York: Academic Press.
50. Morris, C. W. 1938. Foundations of the theory of signs. In O. Neurath, R. Carnap & C.
Morris (Eds.) International Encyclopedia of Unified Science. Chicago: University of
Chicago Press, 77-138.
51. Motschnig-Pitrik, R., Kaasboll, J. 1999. Part-whole relationship categories and their
application in object-oriented analysis. IEEE Trans. on Knowledge and Data Engineering
11(5), 779-797.
52. Motschnig-Pitrik, R., Storey, V. 1995. Modelling of set membership: the notion and the
issues. Data & Knowledge Engineering 16(2), 147-185.
53. Ogden, C., Richards I. 1923. The meaning of meaning. London: Kegan Paul.
54. OMG. 2005. Software process engineering metamodel specification, Version 1.1, January
2005. Available at URL: .
55. Peirce, C. 1955. Philosophical writings of Peirce, edited by J. Buchle. New York: Dover.
56. Pohl, K. 1994. The three dimensions of requirements engineering: a framework and its
application. Information Systems 19(3), 243-258.
57. Punter, T., Lemmen, K. 1996. The MEMA-model: towards a new approach for methods
engineering. Journal of Information and Software Technology 38(4), 295-305.
58. Ralyte, J., Deneckere, R. & Rolland, C. 2003. Towards a generic model for situational
method engineering. In J. Eder & M. Missikoff (Eds.) Proc. of the 15th Int. Conf. on
Advanced Information Systems Engineering (CAiSE’03). LNCS 2681, Berlin: Springer-
Verlag, 95-110.
59. Ramesh, B., Jarke, M. 2001. Towards reference models for requirements traceability.
IEEE Trans. on Software Engineering, 27(1), 58-93.
60. Rolland, C., Prakash, N. 1996. A proposal for context-specific method engineering. In S.
Brinkkemper, K. Lyytinen & R. Welke (Eds.) Proc. of Conf. on Method Engineering:
Principles of Method Construction and Tool Support. London: Chapman & Hall, 191-208.
61. Rolland, C., Prakash, N., Benjamen, A. 1999. A multi-model view of process modeling.
Requirements Engineering 4(4), 169-187.
62. Saeki, M. 2003. Embedding metrics into information systems development methods: an
application of method engineering technique. In Eder J., Missikiff M. (Eds.) Proc. of the
15th Intern. Conf. on Advanced Information Systems Engineering (CAiSE 2003), LNCS
2681, Springer-Verlag: Berlin, 374-389.
63. Saeki, M., Iguchi, K., Wen-yin, K., Shinokara, M. 1993. A meta-model for representing
software specification & design methods. In N. Prakash, C. Rolland & B. Pernici (Eds.)
Proc. of Conf. on Information Systems Development Process. Amsterdam: Elsevier
Science, 149-166.
64. Simon, H. 1960. The new science of management decisions. New York: Harper & Row.
65. Song, X., Osterweil, L. 1992. Towards objective, systematic design-method comparison.
IEEE Software 9(3), 43-53.
66. Sowa, J. 2000. Knowledge representation – logical, philosophical, and computational
foundations. Pacific Grove, CA: Brooks/Cole.
67. Stamper R. 1996. Signs, information, norms and information systems. In B. Holmqvist, P.
Andersen, H. Klein & R. Posner (Eds.) Signs are Work: Semiosis and Information
Processing in Organisations. Berlin: De Gruyter, 349-392.
68. Thayer, R. 1987. Software engineering project management – a top-down view. In R.
Thayer (Ed.) Tutorial: Software Engineering Project Management. IEEE Computer
Society Press, 15-56.
69. Tolvanen, J.-P. 1998. Incremental method engineering with modeling tools – Theoretical
principles and empirical evidence. Dissertation Thesis, Jyväskylä Studies in Computer
Science, Economics and Statistics, No. 47, University of Jyväskylä, Finland.
70. Uschold, M., Gruninger, M. 1996. Ontologies: principles, methods and applications.
Knowledge Engineering Review 11(2), 93-155.
71. Wand, Y., Weber, R. 1990. An ontological model of an information system. IEEE Trans.
on Software Engineering 16(11), 1282-1292.
72. Wastell, D. 1996. The fetish of technique: methodology as a social defence. Information
Systems Journal 6(1), 25-40.
73. Welke, R. 1977. Current information system analysis and design approaches: framework,
overview, comments and conclusions for large – complex information system education.
In R. Buckingham (Ed.) Education and Large Information Systems. Amsterdam: Elsevier
Science, 149-166.