18th European Symposium on Computer Aided Process Engineering – ESCAPE 18
Bertrand Braunschweig and Xavier Joulia (Editors)
© 2008 Elsevier B.V./Ltd. All rights reserved.
OntoCAPE 2.0 – a (Re-)Usable Ontology for
Computer-Aided Process Engineering
Jan Morbach, Andreas Wiesner, Wolfgang Marquardt
AVT-Lehrstuhl für Prozesstechnik, RWTH Aachen University, Templergraben 55, 52056
Aachen, Germany
Abstract
This contribution gives an overview on version 2.0 of OntoCAPE, a formal ontology for
the domain of Computer-Aided Process Engineering (CAPE). We argue that a useful
ontology must simultaneously strive for usability and reusability, explain how these
goals are achieved by OntoCAPE, and present some application examples.
Keywords: Ontology, CAPE, information modeling, knowledge representation.
1. Introduction
An ontology is an explicit specification of a conceptualization, typically involving
classes, their relations, and axioms for clarifying the intended semantics [1]. It
constitutes a structured framework for the storage of information and knowledge, which
can be reused and shared across different software applications. Particularly, ontologies
have been suggested as a means to efficiently build knowledge-based software from
reusable components [2]. To this end, the (generic) ontology must be transformed (i.e.,
extended and customized) into an application-specific knowledge base.
OntoCAPE is a formal ontology designed for use with different types of CAPE tools
that support such diverse tasks as mathematical modeling [3, 4], knowledge
management [5], and data integration [6]. OntoCAPE emerged from two large,
interdisciplinary research projects: the COGents project [3] and the IMPROVE project
[7]. In IMPROVE, the conceptual basis for OntoCAPE was established by the
information model CLiP [8]. Based on CLiP, the COGents project created version 1.0
of OntoCAPE [4], aiming at applications in the area of modeling and simulation. After
the completion of COGents, the further development of OntoCAPE was taken over by
IMPROVE. In 2007, version 2.0 was released [9], which has a broader scope than
version 1.0, additionally aiming at applications in the area of process design. In the
following, we will exclusively refer to OntoCAPE 2.0.
Related efforts are, amongst others, the ISO 15926 Upper Ontology [10] and the POPE
ontology [11]. An extensive review of these and other ontologies, including a
comparison with OntoCAPE, can be found in [12].
The remainder of this paper is organized as follows: Sect. 2 gives an overview on
OntoCAPE; in Sect. 3, the goals of ontology usability and reusability are introduced and
discussed; Sect. 4 explicates how these goals are achieved by the design of OntoCAPE;
Sect. 5 describes some prototypical software applications that demonstrate the practical
applicability of the ontology; Sect. 6 concludes the paper by summarizing our findings.
2. An overview on OntoCAPE
OntoCAPE is organized through three types of structural elements: layers, modules, and
partial models (cf. Fig. 1).
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The layers subdivide OntoCAPE into different levels of abstraction, thus separating
general knowledge from knowledge about particular domains and applications. The
topmost Meta Layer, is the most abstract one; it holds a Meta Model that introduces
fundamental modeling concepts and states the design guidelines for the construction of
the actual ontology. Next, the Upper Layer of OntoCAPE defines the principles of
general systems theory according to which the ontology is organized. On the subjacent
Conceptual Layer, a conceptual model of the CAPE domain is established, which covers
such different areas as unit operations, equipment and machinery, materials and their
thermophysical properties, chemical process behavior, modeling and simulation, and
others. The two bottommost layers refine the conceptual model by adding classes and
relations required for the practical application of the ontology: The ApplicationOriented Layer generically extends the ontology towards certain application areas,
whereas the Application-Specific Layer provides specialized classes and relations for
concrete applications.
notation
Meta Model
Meta
Layer
meta_model
module
module
Layer
Upper
Layer
OntoCAPE
includes
upper_level
system
module
module
partial model
chemical_process_system
Layer
partial model
CPS_realization
Conceptual
Layer
plant
plant_equipment
fixture
machine
Application
Oriented
Layer
Application
Specific
Layer
apparatus
applications
e-procurement
Fig 1: A detail of OntoCAPE demonstrating the overall structure of the ontology
A module assembles a number of interrelated classes, relations, and axioms, which
jointly conceptualize a particular topic (e.g., the module ‘plant’ provides a
conceptualization of chemical plants). The boundaries of a module are to be chosen in
such a way that the module can be designed, adapted, and reused to some extent
independently from other parts of an ontology [13]. A module may include another
module, meaning that if module A includes module B, the ontological definitions
specified in B are valid in A and can thus be directly used (i.e., extended, refined …) in
A. That way, OntoCAPE can be organized as an inclusion hierarchy of loosely coupled
modules.
Modules that address closely related topics are grouped into a common partial model
(e.g., the partial model ‘plant_equipment’ clusters the thematically related modules
‘fixture’, ‘apparatus’, and ‘machine’). Unlike modules, partial models may be nested
OntoCAPE 2.0 – a (Re-)Usable Ontology for Computer-Aided Process Engineering
and may stretch across several layers. While the boundaries of the modules are chosen
for practical considerations (such that the interdependencies between the individual
modules are minimized), the boundaries of the partial models reflect the “natural”
categorization of the domain.
At present, OntoCAPE, including the Meta Model, has a total of 62 modules, which are
clustered in 27 partial models; the modules on the upper three layers (down to and
including the Conceptual Layer) comprise about 500 classes, 200 relations, and 1000
axioms. Additionally, OntoCAPE can be combined with other, specialized ontologies
representing documents, work processes, and decision-making procedures, as described
in [5, 12]. An extensive documentation of OntoCAPE as well as an implementation of
the ontology in the OWL modeling language [14] are accessible at http://www.avt.rwthaachen.de/AVT/index.php?id=486
3. The challenge of (re-)usability
Principally, any ontology has to meet two major goals: to be usable and to be reusable:
The usability of an ontology denotes the degree to which the software component is
useful for a specific task or application. The term also has the connotation of “ease of
use” pertaining to the effort required by a user to operate the system. By definition, an
ontology is never ready for use, but must always be adapted and refined to result in a
knowledge base for the envisioned application. Therefore, the goal of ontology usability
can be phrased as minimizing “the effort required to customize the ontology so that it
can be used by humans or machines in a given application context” [15].
The reusability of an ontology can be defined as “the adaptation capability of an
ontology to arbitrary application contexts” [15], including those contexts “that were not
envisioned at the time of the creation of the ontology” [16]. Note that it is neither
feasible nor desirable to design an ontology that is equally appropriate for all
application contexts [17]; rather, the goal of reusability is to come up with an ontology
that can be adapted to a preferably large number of applications.
Unfortunately, it is difficult to simultaneously achieve high degrees of usability and
reusability: Specializing in one kind of task makes the ontology more useable for this
particular task, but it also decreases the likelihood of its reusability. A highly abstract
ontology, on the other hand, may be applicable to a variety of different tasks, but it is
unlikely to prove very useful for any of these without extensive modification and
detailing. This challenge is known as the reusability-usability trade-off problem [18] in
the literature.
4. Design features of OntoCAPE
To resolve the aforementioned trade-off problem, OntoCAPE adopted a number of
design features that were suggested in the literature. For lack of space, we will only
discuss the most important ones:
First to mention is the structuring of OntoCAPE into levels of abstraction, which has
been recommended by numerous authors (e.g. [16, 17]). According to the trade-off
problem, the general knowledge, which is located on the upper layers of OntoCAPE,
can be reused in a variety of application contexts, but it is not immediately usable. By
contrast, the knowledge located on the lower layers is ready for use, but problemspecific and thus hardly transferable to other applications. Thus, depending on the
respective application, the appropriate level of abstraction for knowledge reuse must be
found. In practice, this means that one needs to move up the ontology (starting from the
Application-Specific Layer) until the encountered knowledge is generic enough to fit
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the respective application context. As an example, consider a CAPE tool, for the
development of which a preferably large part of OntoCAPE is to be reused. The
knowledge on the bottommost layer is application-specific and therefore of little value
for any new tool. But already the above Application-Oriented Layer may contain some
reusable knowledge (provided that the tool operates in an application area that is
covered by OntoCAPE at all). If this is not the case, we need to move up to the
Conceptual Layer; here, at the latest, some reusable knowledge can be found. Thus, one
may reuse the ontology down to and including the Conceptual Layer, at least.
A second design feature recommended by many ontologists (e.g., [13, 19]) is
modularization, that is, the subdivision of the ontology in self-contained modules. By
definition, modules have strong internal coherence but relatively loose coupling with the
other parts of the ontology [17], which facilitates (i) their selective reuse and (ii) the
obligatory customization effort. As for (i), one may choose to reuse only a selected part
of the ontology; in this case, it is relatively simple to cut the connections between the
modules to be reused and the remainder of the ontology. As for (ii), a single module can
be more easily adapted and refined. The reason for this is that the newly defined terms
do not have to be compatible with the entire ontology, but only with a subset, thus
reducing the likelihood of inconsistencies between new and existing ontological
definitions. A positive side-effect of modularization is that the modules are concise and
therefore easier to deal with than a complete ontology, hence bringing further
advantages with respect to maintenance and intelligibility.
A third feature to be mentioned is the Meta Model, which is located on top of
OntoCAPE. Its primary function is to guide the development of OntoCAPE. To this
end, the Meta Model explicitly states the underlying design principles and establishes
general standards for ontology engineering. Due to these properties, the Meta Model has
also proven beneficial for the reuse of the ontology: Firstly, when the ontology needs to
be modified for a particular application, the Meta Model ensures a consistent modeling
style. Secondly, by means of so-called ‘design patterns’ – i.e., templates suggesting best
practice solutions for recurring design problems – the Meta Model provides valuable
guidance for extending the ontology. Thirdly, by examining the Meta Model, new users
can quickly familiarize themselves with the modeling style of the entire ontology.
Consequently, the users may evaluate at an early stage if the ontology is generally
compatible with the target application. This is of special importance, since assessing the
suitability of an ontology for a given application is one of the most time-consuming
tasks in ontology reuse [15].
5. Applications of OntoCAPE
Some prototypical software applications have been developed around OntoCAPE,
which demonstrate the ontology’s capability for reuse as well as its wide range of
applicability:
 In the COGents project [2], OntoCAPE forms part of a multi-agent framework,
which supports the selection and retrieval of suitable process modeling components
from distributed model libraries. Within this framework, OntoCAPE serves as a
communication language between interacting software agents, and between the
software agents and the human users. Concepts from OntoCAPE are used to
formulate a modeling task specification, which is then matched against available
process modeling components also described through OntoCAPE.
 OntoCAPE has also been applied for the computer-aided construction of process
models following an ontology-based approach [4]. This approach constructs a
OntoCAPE 2.0 – a (Re-)Usable Ontology for Computer-Aided Process Engineering
mathematical model in two successive steps. In the first step, a human modeler
constructs a conceptual model of the chemical process; this is accomplished by
selecting, instantiating, and connecting appropriate concepts from OntoCAPE that
reflect the structural and phenomenological properties of the chemical process.
Based on these specifications, the mathematical model is automatically created by a
model generation engine in the second step: The engine selects and retrieves
appropriate model components from a library of model building blocks; these model
components are subsequently customized and aggregated to a mathematical model,
according to the specifications of the conceptual model.
 OntoCAPE has furthermore been reused for the implementation of a Process Data
Warehouse (PDW), which supports the management of experience knowledge in
process design [5]. Within the PDW, OntoCAPE is utilized for the annotation of
electronic documents and data stores. That way, one obtains a consistent, integrated
representation of the contents of these heterogeneous information sources. These
content descriptions can then be processed and evaluated by the semantic searching
and browsing functions of the PDW, which support the navigation between and the
retrieval of information resources.
 Currently, OntoCAPE is applied in an industrial project that is run in cooperation
with partners from the chemical and software industries. The goal of this project is
the development of an ontology-based software prototype for the integration and
reconciliation of engineering data from distributed information sources [6].
In each reuse cycle, OntoCAPE has been tested and revised. As part of this process, a
number of modeling errors and inconsistencies have been detected and eliminated.
Moreover, the ontology has been remodeled such way that it is now applicable to a large
number of tasks. Consequently, the quality of the ontology has gradually improved
throughout these projects.
6. Conclusion
We have presented OntoCAPE, a formal ontology for the domain of computer-aided
process engineering that is explicitly designed for reuse. An implementation of
OntoCAPE in OWL, supplemented by extensive documentation, can be downloaded
free of charge at our website.
OntoCAPE’s modular, layered structure, supports the partial, selective reuse of the
ontology and allows an easy adaptation to various task requirements. The Meta Model
provides guidance for extensions and modifications of the ontology and enables a fast
screening for compatibility with application requirements.
OntoCAPE has been applied in a number of software projects covering the application
areas of modeling and simulation as well as process design and engineering. In the
course of these projects, the quality of the ontology has gradually improved: Some
former errors and deficiencies could be detected and corrected. Moreover, certain parts
of the ontology have been reformulated – either to better support efficient reasoning or
to enable a more user-friendly representation. As a result, OntoCAPE now constitutes a
validated ontology of proven usability, with a high degree of reusability.
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