USE OF EXISTING ONTOLOGIES AS INPUT FOR

STRUCTURAL COMPLEXITY MANAGEMENT

Reducing the Effort for Analysing and Improving Engineering Systems

Andreas Kohn, Maik Maurer, Helena X. Schmidt and Udo Lindemann

Institute of Product Development, Technische Universität München, Boltzmannstraße 15, 85748, Garching, Germany

Keywords: System modelling, System analysis, System improvement, Reuse of knowledge, Structural complexity

management, Multiple domain matrix, Structure computation.

Abstract: This paper presents an approach for combining two actual trends in the engineering domain: ontology-based

knowledge management and structural complexity management. A focussed engineering system can be

analysed and possibilities for improvements can be deduced with low effort by applying structure based

algorithms on already existing ontologies. An overview of the current use of ontologies in the engineering

domain is given for showing the various options for this application of structural complexity management.

Necessary interfaces between ontology-based knowledge management and matrix-based structural

complexity management are deduced by comparing both approaches considering data representation and

analysis capabilities. The proposed approach is applied and discussed by the example of analysing an

ontology originally developed for handling technical solution knowledge in the field of automation industry.

1 INTRODUCTION

Knowledge management has an increasing influence

on the success of companies. Corresponding to the

overall current trend in knowledge management,

ontologies play an increasing role in knowledge

management in the engineering domain (Kim et al.,

2008). Here, knowledge management systems using

ontologies can be found in the various different

knowledge-intensive applications for modelling,

storing and providing the required knowledge. The

goals of using ontologies in this field correspond to

the overall goals of using ontologies in knowledge

management described by (Tudorache, 2006).

Ontologies in the engineering domain are used for

sharing a common understanding of the respective

domain and enable knowledge sharing between

humans and software applications. As a

consequence, they enable sharing the terminology

defined in the ontology. Furthermore, they make

domain knowledge explicit and make reasoning

about it possible. By having a formal representation,

the knowledge can be used in formal reasoning and

querying algorithms. Finally, ontologies permit the

reuse of existing knowledge and improve the

consistency of the regarded information.

Another actual trend in the engineering domain

is the need for handling the complexity in nowadays

engineering tasks. In almost all relevant sections of

engineering, a steady increase of complexity can be

observed (Lindemann et al., 2009). This complexity

results from the high, possibly time-variant number

of elements and relations that have to be considered.

The Structural Complexity Management (StCM)

methodology offers generic methods for modelling

and analysing the underlying structure of these

complex systems (Lindemann et al., 2009). The

analyses are computed in a so-called Multiple

Domain Matrix (MDM) and are used to identify

potential improvements for the focused system.

The research presented in this paper aims at

combining these two actual trends in the engineering

domain. We claim that StCM methodology can

increase the system understanding for the companies

by applying the structural analysing algorithms on

the knowledge already stored in existing ontologies.

This goes beyond the current use of ontologies in

knowledge management (e.g. for knowledge storing

and querying). Shortcomings and problems of the

respective systems can be revealed by applying

analysis algorithms of StCM, and subsequently lead

to an improvement of the system. As the information

195

Kohn A., Maurer M., X. Schmidt H. and Lindemann U..

USE OF EXISTING ONTOLOGIES AS INPUT FOR STRUCTURAL COMPLEXITY MANAGEMENT - Reducing the Effort for Analysing and Improving

Engineering Systems.

DOI: 10.5220/0003635701950201

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 195-201

ISBN: 978-989-8425-80-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

needed for the analysis is already stored in existing

ontologies, the additional effort is quite low in

comparison to the possible profit for the companies.

By using existing ontologies, the actual high amount

of time for information acquisition in StCM is

shortened, the required efforts for system modelling

are reduced and the efficient application of the

approach is guaranteed.

According to this main hypothesis, the paper is

structured as follows: first, we will introduce the

steps of StCM methodology and show how systems

can be analysed and improved by applying structural

computation algorithms. Then, we will give an

overview about the use of ontologies in the

engineering domain. This shows the various

possibilities for applying the structural analysis

algorithms on existing ontology-based knowledge

repositories. In the following chapter, we will

compare ontology data representation with the data

representation needed for StCM methodology. This

builds the basis for our solution approach for

transferring information stored in ontologies to the

MDM. The proposed approach will be described and

explained by the example of an ontology used for

storing information about existing technical

solutions in the automation industry. We will

conclude this paper with a discussion of the

presented approach and an outlook on the next steps.

2 STATE OF THE ART

2.1 Structural Complexity

Management

This section provides a short introduction to StCM

theory in order to allow the reader to follow the

main topic of this contribution. Details of the

method can be taken from Lindemann et al. (2009).

Figure 1 shows a system description by elements

and relations between them. The elements belong to

three domains (indicated by circles, squares and

triangles). In terms of engineering, these domains

could stand for components, people and functions.

On the left side, the system is visualised by a graph

representation. On the right side, the systems is

modelled in a MDM. This comprehensive matrix

model consists of Design Structure Matrices

(DSMs), which contain relations between elements

belonging to only one single domain and Domain

Mapping Matrices (DMMs), which contain relations

between elements belonging to two different

domains. For example, a DSM describes the links

between system functions, whereas a DMM

indicates which people are responsible for which

components in design.

Figure 1: Structure of a MDM.

The MDM represents the core of the StCM

methodology that provides a five-step procedure to

support users in system definition, information

acquisition, deduction of indirect dependencies,

structure analysis, and the application on the product

design. The initial situation for the application of the

approach is a handling or design problem due to the

system’s complexity. In the first step of system

definition it is clarified which system aspects

(domains and relations) have to be considered in

order to solve the complex problem. Next,

information about specific system elements and their

direct dependencies have to be acquired. This step

represents the highest effort of time within the

approach. At this point, the proposed approach in

this paper can enable benefit in time and quality as

already structure information stored in ontologies

can be easily reused. Whereas only direct

dependencies are acquired in the previous step, now

indirect dependencies are deduced – in case they are

required for solving the initial complex problem. For

example, relations between components of a product

can be deduced on the basis of the performed

functions. Based on acquired and deduced structural

information, now relevant system structures are

analysed. The objective is to identify characteristic

constellations, which allow interpretations and

system optimization in the following. Finally,

findings have to be applied in order to solve the

initial problem. Thus the result of the final step of

the StCM approach is the improved system

management or design due to the application of

gained structural understanding.

2.2 Use of Ontologies in the

Engineering Domain

As possible input for StCM, this section will review

the use of ontologies in the engineering domain and

classify the scope of the ontologies according to

DSM DMM

MDM

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their general objectives (product, process,

organization) and their area of application.

In a quite exhaustive comparative study, thirteen

approaches for dealing with different types of

engineering problems by applying ontology-based

knowledge management are presented (Kim et al.,

2008). Seven of them focus on product knowledge,

four on process knowledge, and two model both

knowledge about product and process (for details

about the different ontologies and projects, please

see Kim et al. 2008 or the original literature).

Beyond that, several more ontologies in the

engineering domain were identified in the present

research. Most of them focus on the early phases in

the lifecycle of a product, that of product

development. (Gaag et al., 2009) developed a

product-focused ontology for modelling knowledge

about existing technical solutions which supports the

automatic annotating of existing solution documents

and the retrieval of the stored information in the

field of automation industry. Furthermore, an

ontology is developed and used for integrating CAx-

Systems in the step of virtual and physical validation

of parts and prototypes in the automotive sector

(Syldatke et al., 2008). Tudorache proposed a

generic product ontology that is validated in two

scenarios of requirements management and

concurrent engineering (Tudorache, 2006). An

ontology for improving design communication

which contains process-related knowledge was

developed and applied for improving design

collaboration (Uflacker et al., 2009). Furthermore,

Darlington and Culley practically evaluated the use

of ontologies in “requirements development and

capture” as an important phase of engineering design

(Darlington and Culley, 2008). In addition, a

process-oriented ontology was developed to support

the quality-assurance process in the field of

electronics design (Yang, 2005). Although, Anderl

et al. do not present a precise ontology, they also

emphasise the increasing importance of ontologies

for product development (Anderl et al., 2009). They

propose an ontology-based-product development

system that implements the management of access

rights for different user groups and functionalities

for integrating, releasing and storing information.

Ontologies in the engineering domain are also

applied for enhancing manufacturing systems’

intelligence. For example, a cognitive machine shop

is proposed where machines “know” about their

manufacturing capabilities by representing the

relevant knowledge about material, work pieces, etc.

in an machine-interpretable ontology (Shea et al.,

2010).

3 USING ONTOLGIES AS INPUT

FOR StCM

This section provides the basis for the proposed

approach by showing similarities and differences

between knowledge management using ontologies

and StCM. We will discuss the data representation

capabilities of ontologies and MDMs to identify

possible losses during the transformation of

information from an ontology to a MDM. Finally we

will show the analysis and computation capabilities

of StCM and deduce the constraints concerning the

needed information input from ontologies.

3.1 Data Representation

The main elements of domain knowledge in

ontologies are concepts, relations, functions,

procedures, instances, axioms and production rules

(Corcho and Gómez-Pérez, 2000). For each of these

main elements, ontology languages provide various

different features. For example, for describing

concepts in an ontology, features like meta-classes,

definition of attributes and definition of properties of

attributes can be used in most of the languages.

In contrary to this feature-variety in ontology

languages, the data representation used in StCM is –

on the first glance – quite simple-constructed. In a

standard MDM the focus lies on domains, elements

of a domain, relations between the elements and

attributes of elements or relations. This can be

explained by the fact that MDM theory is based on a

matrix and graph representation of the system.

Therefore, features like meta-classes (e.g. for

building a taxonomy of classes) or functional

relations between elements are mostly not focused.

Approaches for modelling this enhanced knowledge

representation that burst the bounds of traditional

matrix-based constraints exist, but are still not

common. For example, logical dependencies (which

can be interpreted as production rules in ontology

modelling) were implemented and evaluated in the

so-called “why-matrix” (Maurer and Braun, 2008).

A hierarchical view of the system and the

introduction of a hierarchy for the domains was

proposed for the reduction of efforts for data

acquisition (Biedermann et al., 2010). Also, the “1.5

Matrix” allows a hierarchical view on the elements

of a system by interpreting attributes of elements as

meta-classes (Eppinger, 2009). A further approach

aims at overcoming the strictly domain-oriented

representation of a system in form of a MDM by

proposing flexible domain modelling (Kohn and

Lindemann, 2010).

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Summing up this comparison of data

representation, it can be said that not all information

stored in an ontology can be transferred into a

MDM. Nevertheless, all information needed for

analysing complex systems in StCM methodology

(domains, elements and relations) can be found

structured in ontologies. Therefore, ontologies can

generally be used as information input for StCM.

3.2 Analysis Capabilities

In a further step, we will now describe the

computation and analysis capabilities of StCM and

discuss the resulting constraints for the needed

information input from ontologies.

In StCM methodology, there are two main types

of computations. First of all, indirect dependencies

are deduced in the third step of StCM. Here, the

direct dependencies modelled in the DSMs or

DMMs are used for the computation of further

DSMs or DMMs (which then contain indirect

dependencies between their elements). This is done

by applying computation algorithms from the field

of matrix algebra. Several types of indirect

dependencies exist. For example, a DSM can be

computed using only one DMM, or using another

DSM and two DMMs. The degree of indirect

dependency provides information about the strength

and therefore the relevance of the indirect

dependency. Both the direct dependencies and the

indirect dependencies are then used for the second

type of computations in StCM: the structural

analysis algorithms. Using the analysis algorithms,

structural significant subparts of the structure can be

identified. These algorithms are often matrix-based

(e.g. clustering algorithm for static matrices, tearing

algorithm for time-based matrices, etc.) or graph-

based (e.g. leaves of a structure, significant hubs,

etc.). In the next step, the significant subparts of the

structure serve for indicating possible improvement

potential for the system according to the initial goal

of the analysis. Established matrix- and graph-based

visualisation techniques are used for showing and

presenting the results and serve for enhancing the

system understanding.

For the import of information from an ontology,

one critical point has to be mentioned concerning the

analysis capabilities: all computations done in StCM

analysis are based on the elements in a MDM and

their relations. Without the elements, the algorithms

and therefore the analysis would be obsolete. In

contrast, ontologies do not need the instances for

reasoning or querying an ontology. They can

perform computation only on the taxonomic level of

the ontology (also referred to as T-Box – among

others (Struckenschmidt, 2009)). Thus, structural

analysis algorithms can only be applied on

ontologies that contain instances and have a well-

populated T-Box. But, as current trends in ontology

design goes from pure taxonomic ontologies towards

descriptive and instantiated ontologies (Kim et al.,

2008), this restriction becomes less significant.

4 APPLYING StCM ON EXISTING

ONTOLOGIES IN THE

ENGINNERING DOMAIN

The initial question when using StCM on the basis

of existing ontologies is whether the information

modelled in the ontology can serve for solving a

handling or design problem. Therefore, it has to be

clarified if the concepts and relations of the ontology

and the contained elements suffice for the intended

analysis of the system. In some cases, only part of

the information stored in the ontology can be used

and, consequently, further information sources have

to be taken into account. The relevant and useful

concepts and relations for the initial problem are

then taken for building the meta-model.

Subsequently, the elements and their relations in the

T-Box of the ontology can be transferred into the

matrices of the MDM in the step of information

acquisition. The next three steps of deducing indirect

dependencies, analysing the structure and

application on the system are equal to the original

StCM methodology.

By the example of the application in a use case in

the field of the automation industry, we will

illustrate the presented approach and discuss the

benefit of the further analysis capabilities described

above. The developed ontology is an OWL-DL

ontology and contains information about existing

technical solutions (Gaag et al., 2009). This

ontology is used for supporting the annotation of

existing solution documents and enables their

subsequent retrieval by providing different

abstraction principles for the similarity of technical

solutions (Kohn and Lindemann, 2011).

The following Figure 2 shows an excerpt of the

main concepts and properties of the ontology with

an exemplary instantiation. A company provides a

technical solution that is used in an industrial sector.

The technical solution uses function owners that

perform certain functions. For example, a robot

performs the function “transfer bottle” and the

cylinder performs the function “fill bottle”.

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198

Figure 2: Ontology for describing solution documents with

exemplary instantiation.

In our example, we use parts of this ontology to

identify possibilities to modularise the technical

system by reorganising the function owners into

modules. In the engineering domain, this can be

done by reorganising and grouping functions owners

according to the performed functions. Therefore, we

will transform the needed information for this design

problem into the meta-model in form of a MDM.

The concepts of the ontology are interpreted as

domains in the MDM and the properties are the

relations between the domains (see Figure 3).

Figure 3: Meta-model of the system in form of a MDM.

In our case, we only need information about

function owners and functions (in Figure 3 labelled

as number 1). In a next step, the instances and the

relations between instances are transferred from the

ontology to this matrix. Properties of instances

become relations of elements in the system. For

transferring the information in the ontology, the

property “perform” of the function owner “robot” to

the function “transfer bottle” becomes a cross in the

appropriate field in the DMM “function owner

performs function” (Figure 4 a). In a further step,

indirect dependencies can be deduced for showing

the dependencies between function owners. In this

example, indirect dependencies between function

owners were computed from the initial DMM and

stored in a new DSM with to the relation “two

function owners are related when they perform the

same function” (in Figure 3 labelled as number 2).

In a next step, this newly calculated DSM with

indirect dependencies can be used for analysis

algorithms. Here, the result of a clustering algorithm

is shown in a matrix-based visualisation (Figure 4 c).

In this case, two clusters are clearly visible. In terms

of engineering the clusters indicate possibilities for a

functional modularisation of these function owners,

as they perform the same functions. Beyond the

matrix-based analysis, graph-based analysis can be

used (Figure 4 d). The elements of the system are

visualised as the nodes of the graph and the relations

as edges. As structural significant system elements, a

bridge element connects the two clusters identified

above. If this element fails, both clusters would be

affected. For our modularisation approach, this

element can build the hub between the two modules.

Figure 4: Applying computation, analysis and

visualisation algorithms on the MDM.

The example provides a short glance on the

possible application of the presented approach. It

showed that information stored in an ontology that

was originally developed for a different purpose can

be successfully used for analysing the underlying

system. Possibilities for system improvements

concerning a certain design problem can be

identified. As the needed information is already

stored in the ontology, the effort for the otherwise

time-consuming information acquisition is very low.

function owner

function

performs

transf er bottle

robot

technical

solution

uses

bottling line

company

provides

GoodFill

Corp.

industrial sector

packaging

industry

uses

cylinder

fill bottle

comp any

technical

solution

industrial

sector

function

owner

function

comp any provides

technical

solution

uses

industrial

sector

uses

function

owner

performs

function

Indirect

dependencies

function owner

function ownerfunction

function owner

cluster

Indirect dependencies

Initial DMM

Clustering algorithm Graph visualisation

bridge

a b

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5 RELATED WORK AND

DISCUSSION

A study of related work revealed that combining

matrix methodologies and ontologies was presented

by different authors before. However, they

combined matrix methodologies and ontologies each

in a very specific use-case. They did not consider

and evaluate the general possibilities for interaction

of the two approaches as presented in this research.

For example, Yang transforms information stored in

an ontology to a Domain Structure Matrix in a

process framework for consumer electronic design.

He uses the computation capabilities in the DSM for

analysing activity dependencies in an ARIS

(Architecture for Information Systems) process for

removing cyclic dependencies in form of interaction

loops (Yang, 2005). Syldatke et al. propose the

combination of ontologies and DSM-modelling

techniques. They show the possibilities of enhancing

DSM-modelling techniques by using semantic

technologies (e.g. ontology languages and reasoning

capabilities), but did not go into detail considering

the conditions for the proposed combination

(Syldatke et al., 2008). Finally, existing research

reveals a study of using ontologies for the

integration of DSM analysis techniques into the

planning of construction projects. Here, once again

the analysis capabilities of matrix-based algorithms

are emphasised and evaluated in the specific use

case (Masera, 2007).

Summing up, two different ways of combining

the two approaches can be found. First, – as shown

in this paper – structural analysis algorithms on the

basis of DSM, DMM or MDM applications can be

used for enhancing current ontology-based

knowledge modelling approaches. The other

possibility aims at the opposite direction. Here,

achievements of established semantic technologies

are proposed to be used for enhancing representation

and modelling capabilities of matrix-based

approaches. Both approaches can be useful

depending on the intended purpose and reveal

interesting options for the combination and

integration of both approaches.

Regarding the comparison between ontology-

based knowledge management and StCM done in

this research, the general different scope of these

two fields has to be discussed. As shown above, the

StCM methodology is directly focused on the

structural analysis of complex systems. In contrast,

ontologies and ontology development in general

have no real focus on a certain application per se. It

depends strongly on the individual specification and

the desired objectives. Therefore, the capabilities of

ontologies are more generic and have to be

individually specified corresponding to the

application in the respective use-case. This

generality can be both benefit and disadvantage. For

experts in knowledge engineering this is certainly an

advantage. However, for people in the engineering

domain, who are often not experienced in building

and using ontologies (Kim et al., 2008), predefined

analysis criteria as proposed by the StCM

methodology could be helpful. Also, the

visualisation part and the comprehensibility have to

be taken into account. Matrix-based and graph-based

visualisations have proved to increase system

understanding and help people handling complex

systems.

As shown in the previous chapter, an existing

ontology can only be used for design or handling

problems that require information which is – either

direct or indirect – available in the ontology. The

quality of the ontology influences the results of the

analysis. If the concepts or relations do not provide

the needed information or not in satisfying quality,

the ontology cannot be used for the specific

problem. Then, the definition of the meta-model and

the information acquisition has to be done according

to the standard StCM methodology.

6 CONCLUSIONS AND

OUTLOOK

The research presented in this paper gives an insight

into possibilities for the combination of knowledge

management with ontologies and StCM in the

engineering domain. By applying analysis

algorithms on already existing ontologies, the

system understanding can be enlarged and

possibilities for system improvement according to

the analysis results can be deduced. The literature

review on existing approaches using ontologies for

handling engineering problems reveals the

increasing use of ontologies in the engineering

domain. This provides the basis needed for the

presented approach and confirms its further

application possibilities.

The transformation of information stored in

ontologies to MDM needed for the structural

analysis is shown by comparing ontologies and

StCM according to their data structure and analysing

capabilities. An example from the automation

industry illustrated the presented approach. Here, an

ontology originally designed for capturing

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knowledge about already existing technical solution

was exemplarily analysed by selected structural

analysis algorithms for the identification of

modularisation potential in the system. This

application showed that structural analysis

algorithms can enhance the analysis of systems

already modelled in an ontology with very low

information acquisition effort.

Further work will focus on the above mentioned

bidirectional combination and the integration of

ontology-based knowledge management and StCM

in combination with further empirical studies. Using

structural analysis algorithms and therewith enabling

the corresponding system improvement possibilities

directly with an ontology-based system seems to be

very promising. Therefore, the already existing

analysis algorithms StCM have to be translated into

ontology-interpretable defined classes for enabling

reasoning or the appropriate ontology queries.

ACKNOWLEDGEMENTS

Part of this work has been funded by the German

Federal Ministry of Economy and Technology

(BMWi) through THESEUS.

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