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Journal ArticleDOI

7.5.1 An incremental hybridisation of heterogeneous case studies to develop an ontology for capability engineering

01 Jul 2012-Vol. 22, Iss: 1, pp 956-971

TL;DR: An analysis of perspectives for “capability engineering” has been conducted and a single shared ontology for the concept of capability engineering is developed to enable semantic interoperability and to support a formal and explicit specification of a shared conceptualisation.

AbstractAn analysis of perspectives for “capability engineering” has been conducted by the INCOSE UK Capability Working Group (CWG). This paper is a continuation of this study led by the CWG ontology work stream that aims to develop a single shared ontology for the concept of capability engineering to enable semantic interoperability and to support a formal and explicit specification of a shared conceptualisation. Case study material from the different domains of rail, defence and information services was used. The ontology development was executed in three phases; (1) pre-analysis, (2) ontology modelling and (3) post-analysis. The pre-analysis involved literature reviews, requirements specification, systems engineering process utilisation; and resource identification i.e. examination of the case study material. The ontology modelling phase comprised information extraction and classification in addition to modelling and code representation using a mark-up tool, MS Excel and Protege. The post-analysis involved validation workshops through using expert focus groups.

Summary (3 min read)

Introduction

  • This paper is primarily based on the research conducted by the INCOSE UK Capability Working Group (CWG) which identified eight perspectives of capability and developed an entity relationship diagram for the concept of capability engineering (Henshaw et al. 2010).
  • The holistic thinking mindset, socio-technical nature and similarity in scope to views of systems engineering are emphasised to elaborate the significant difference from product systems engineering and broader characteristics to process perspective of systems engineering.
  • The scope of capability engineering is large and there are challenges in producing an all-embracing model with a vocabulary agreed by all parties.
  • A case study based information extraction approach that looked at the problem space in chunks is described within this paper.

Objectives

  • A set of high level objectives are derived through analysis of the CWG workshop discussions and prior meetings with the CWG ontology work stream members: Objective 1: to develop a sector-independent ontology by extracting and classifying information from case studies across heterogeneous domains to explicitly specify the concept of capability engineering.
  • To evaluate (verify and validate) the ontology through expert reviews and application to a case study, also known as Objective 3.
  • The next section discusses the current ontology-based approaches and proposes a flow chart for the development of the capability engineering ontology.

Ontology-Based Approaches

  • Ontology-based modelling for information and knowledge management has been widely used (Hughes et al.
  • These are discussed later within the requirements analysis section.
  • A variety of methods, methodologies, tools and languages for ontology development have already been analysed by various authors (Corcho et al.
  • The pre-analysis phase predominantly focuses on case studies.

Case Studies

  • Appropriateness of case study material Case studies are relatively important when extracting the necessary information and evaluating the ontology developed.
  • A mark-up tool is used to extract important terminology from the case studies and Protégé to construct the ontology i.e. concepts and their relationships.
  • Examples from these case studies are used to explore and illustrate their relation to the worldviews (Ws) of capability (Table 1) as described by the INCOSE UK Capability Working Group (Henshaw et al. 2010).
  • W2. Capability Planning – A set of explicit user wants e.g. “delivering 50K passengers an hour in the peak” is translated into a written set of solution independent requirements and systems design options to satisfy the capability needs e.g. 24 trains per hour, new signalling, automatic train operation, new rolling stock and new franchises.
  • An enterprise of users and suppliers including the MoD and industry develop and operate carrier strike capability solution (that aims to project UK Air Power on an expeditionary basis) across all contributing components of capability from equipment to infrastructure (i.e. JCA integration with the ship, dockyard developments) by deploying all appropriate systems engineering approaches and techniques from cradle to grave.

Requirements Analysis

  • This section reports on the requirements of the overall study.
  • This involves employing an iterative requirements analysis approach to capture, analyse, and synthesise the key stakeholder requirements.
  • Since this is not a product or software development project, the requirements analysis study used techniques from a synthesis of approaches including the Quality Function Deployment (QFD), Unified Modelling Language (UML) and the Volere Requirements Specification (VRS).
  • The requirements specification pro-forma listing these requirements is extracted from the hybrid of STA and VRS template as shown in Figure 3.
  • The stakeholders are divided into three groups; professional body, industrial and academic.

Ontology Development

  • This section exploits the process flow chart illustrated in Figure 1 to develop an ontology.
  • The pre-analysis, modelling and post-analysis phases are described further.

Pre-analysis

  • The pre-analysis involved a literature review, requirements analysis and resource identification as described earlier.
  • Examination of the case studies, reports and documents to check their relevance and richness for information extraction has already been discussed.
  • Existing ontologies and approaches e.g. ontology for product-service system (Annamalai et al. 2010) have been analysed to build a process flow chart that encapsulated a procedure for ontology development.

Ontology modelling

  • The ontology modelling phase involved information extraction and classification.
  • Protégé is “a free, open-source platform that provides a growing user community with a suite of tools to construct domain models and knowledge-based applications with ontologies” (Protégé 2011).
  • This task involved importing the terms or concepts extracted through the mark-up tool into MS Excel for further analysis.
  • The analysis comprised subsequent grouping of these terms into more general concepts; generating statements showing the relationship and context of use of the terms; adding new concepts that are not capture by the ERD classification; and also generating RDF n-triples by using the subject-predicate-object expressions to denote the relationships.
  • A total of 157 n-triples were created for INCOSE CWG perspective analysis case study on its own.

Post-analysis

  • The post-analysis phase is concerned with ontology evaluation and therefore comprises the validation of the ontology through expert reviews, user feedback and application to a case study to check its suitability.
  • The evolution of the ontology is significantly important as procedures need to be put in place for keeping the ontology revised and up to date i.e. adding, deleting or modifying the ontology to incorporate boundary changes.
  • The outcome of the three phases resulted in the initial ontology as discussed in the next section.

Analysis of initial results

  • The term extraction results for three of the case studies are summarised in Figure 7.
  • The NPfIT and CSAR case studies are parked to be used when evaluating the finalised ontology.
  • Figure 9 shows the initial results for capability engineering ontology v1.1.
  • The use of ‘AnEnterprise’ and ‘Organisations’ can be expressed through the cardinality constraints.

Discussion

  • This research proposed a process flow chart for incremental ontology development though using case study material from heterogeneous domains.
  • It also presents and discusses initial findings from a study that develops an ontology for capability engineering.
  • The RDF n-triples results are from three case studies with two of them being validated through expert focus groups.
  • This task is partly automated but predicates such as ‘subClassOf’, ‘consistsOf’, ‘comprise’ and ‘encompasses’ require further human assisted analysis for insertion into Protégé.
  • The final version of the ontology can be an enabler to develop such tools.

Conclusion

  • This paper presents the first steps towards establishing an ontology to enable semantic interoperability in order to support institutionalisation of the concept of capability engineering.
  • The task of coming up with a common set of concepts, properties and relationships to enable a standard domain terminology and common understanding within heterogeneous domains is significant, because this will be an enabler of cross-domain knowledge sharing.
  • The capability engineering paradigm is gaining attention in several domains and signals increased efforts to manage large and complex systems more effectively from the acquisition and operational perspectives.
  • The remaining challenges for this work include getting general acceptance and development of a hierarchy.
  • The final version of the ontology will be imported into Protégé to support collaborative discussions i.e. seeking international feedback over the internet.

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An incremental hybridisation of heterogeneous case
studies to develop an ontology for capability
engineering
Huseyin Dogan
Engineering Systems of Systems
Group
Loughborough University, UK
Michael J de C. Henshaw
Engineering Systems of Systems
Group
Loughborough University, UK
Julian Johnson
BAE Systems, UK
Copyright © 2012 by Huseyin Dogan, Michael J de C. Henshaw and Julian Johnson.
Permission granted to INCOSE to publish and use.
Abstract. An analysis of perspectives for “capability engineering” has been conducted by the
INCOSE UK Capability Working Group (CWG). This paper is a continuation of this study
led by the CWG ontology work stream that aims to develop a single shared ontology for the
concept of capability engineering to enable semantic interoperability and to support a formal
and explicit specification of a shared conceptualisation. Case study material from the
different domains of rail, defence and information services was used. The ontology
development was executed in three phases; (1) pre-analysis, (2) ontology modelling and (3)
post-analysis. The pre-analysis involved literature reviews, requirements specification,
systems engineering process utilisation; and resource identification i.e. examination of the
case study material. The ontology modelling phase comprised information extraction and
classification in addition to modelling and code representation using a mark-up tool, MS
Excel and Protégé. The post-analysis involved validation workshops through using expert
focus groups.
Introduction
This paper is primarily based on the research conducted by the INCOSE UK Capability
Working Group (CWG) which identified eight perspectives of capability and developed an
entity relationship diagram for the concept of capability engineering (Henshaw et al. 2010).
The CWG perspectives analysis sub-group view ‘capability’ as the ability to do something
which has an overarching approach that links value, purpose, and solution of a systems
problem. The holistic thinking mindset, socio-technical nature and similarity in scope to
views of systems engineering are emphasised to elaborate the significant difference from
product systems engineering and broader characteristics to process perspective of systems
engineering.
This paper introduces rigour, richness and detail to the concept of capability engineering
thorough development of a user-centred, systematic and case study based ontology. Noy and
McGuiness (2000) describe the rationale behind developing an ontology as; to share common
understanding of the structure of information to enable semantic interoperability; to enable
the reuse of domain knowledge through a data structure and conceptual schema; to make
domain assumptions explicit; and to analyse domain knowledge. Semantic interoperability is
the ability to exchange data in order to improve interoperability between systems. The scope
of capability engineering is large and there are challenges in producing an all-embracing
model with a vocabulary agreed by all parties. Modelling the whole space in one set of a
language can be difficult. A case study based information extraction approach that looked at

the problem space in chunks (domains) is described within this paper. The progressive
updating of the ontology due to evolving nature of the problem space is discussed by using
the initial validation results from the focus groups.
Objectives
A set of high level objectives are derived through analysis of the CWG workshop discussions
and prior meetings with the CWG ontology work stream members:
Objective 1: to develop a sector-independent ontology by extracting and classifying
information from case studies across heterogeneous domains to explicitly specify the
concept of capability engineering.
Objective 2: to develop an information model from the ontology that is to be informed
by the capability engineering activity model to support the ‘management of
knowledge’ within the context of capability engineering.
Objective 3: to evaluate (verify and validate) the ontology through expert reviews and
application to a case study.
The next section discusses the current ontology-based approaches and proposes a flow chart
for the development of the capability engineering ontology.
Ontology-Based Approaches
Ontology-based modelling for information and knowledge management has been widely used
(Hughes et al. 2009; Duan et al. 2009; Dongmin et al. 2010; Liu et al. 2010). Generally, an
ontology is defined as an explicit specification of a conceptualisation where concepts and
their relations are extracted from the real world (Studer et al. 1998; Duan et al. 2009). An
ontology-based approach is complementary to more conventional modelling approaches such
as Unified Modelling Language (UML) and Systems Modelling Language (SysML) because
an ontology is significantly useful in defining a domain from multiple author perspectives
and terminologies. Allemang 2008 elaborate one of the tenets of the Semantic web “AAA;
Anyone can say Anything about Any topic”. In contrast to conventional modelling, ontology
model has formalisms and associated inferencing that facilitate bringing multiple user
perspectives and vocabularies together. However, like conventional modelling, it also
focuses on the key concepts, i.e. “what is”, as in, “what is capability engineering and how is it
different from other engineering domains?”. The classes and relationships typically
associated with the model can also be added.
A single shared ontology for capability engineering can enable semantic interoperability and
support a formal and explicit specification of a shared conceptualisation. As recommended by
Unshold and Gruniger (2004) when developing a practical single shared upper ontology, the
mapping of ontology amongst domains and the eight different worldviews identified by the
INCOSE UK CWG (e.g. equipment, organisational and service centric worldviews) needs to
be human assisted rather than fully automated to achieve the interoperability,
interconnectedness and correlation desired between these heterogeneous domains. The
ontology shall also form tight definitions and be independent of industry sector and
applications. These are discussed later within the requirements analysis section.
A variety of methods, methodologies, tools and languages for ontology development have
already been analysed by various authors (Corcho et al. 2002; Mizoguchi 2003; Mizoguchi

and Kozaki 2009). Languages such as OWL (Web Ontology Lanaguage) and IDEF5
(Integrated Definition for Ontology Description Capture Method) in addition to tools
including Protégé and Hozo are examined to check their applicability. A process flow chart is
also proposed for developing an ontology for capability engineering as a result of analysing a
variety of developments including
ontology-based conceptual knowledge representation model (Kourlimpinis et al.
2008);
ontology-based information model development for science information reuse and
integration (Hughes et al. 2009);
ontology-based knowledge modelling framework for intangible cultural heritage (Tan
et al. 2009);
domain ontology life-cycle engineering framework for modular product design (Duan
et al., 2009; Liu et al. 2011); and
ontology for product-service system by Cranfield University (Annamalai et al. 2010).
Annamalai et al. (2010) discusses methodologies for developing an ontology through
analysing various approaches and argued that “there is no one correct way to model a domain
and that ontology development is necessarily an iterative process”. Although there are major
similarities with these reviewed approaches, the details and context vary. As illustrated in
Figure 1, a process flow chart is proposed that incorporates the findings from the above to
enhance ontology development through pre-analysis, modelling and post-analysis phases.
The pre-analysis phase predominantly focuses on case studies. These are discussed next.
Case Studies
Appropriateness of case study material
Case studies are relatively important when extracting the necessary information and
evaluating the ontology developed. Post-analysis evaluation through application to a specific
case study can support the validation and refinement of the ontology. The INCOSE UK
Capability Working Group was approached to identify the appropriate case studies in
heterogeneous domains including defence, rail and information services; progress is reported
on these domains herein and further analysis is being conducted in other domains. A list of
features that makes the case studies appropriate is derived to guide the decision on case study
selection.
Does the case study contain sufficient material of relevance to our definition of
capability (i.e. ability to do something) and can we map this onto the capability
engineering entity relationship diagram?
Which domain does the case study belong to e.g. defence (land/air/sea), rail, health?
How can we determine the level of abstraction the case study material address e.g.
generic domain, collaborative programme, specific project?
Is there a concise but substantial source that provides detailed information about this
case study?
Is this case study document in a form (e.g. MS Word) that is easy to process?
What worldviews of the CWG white paper does this case study address?
What will be the applications of an ontology for this particular case study?

Figure 1. Process flow chart for ontology development
As shown in Figure 2, various case study options have been analysed to determine their
relevance to developing a capability engineering ontology. For example, the Queen Elizabeth
Class (QEC)
1
contributes to a high level capability (i.e. the carrier strike capability) and can
have capabilities within itself (UKMoD 2012). The case studies in Figure 2 are discussed
with regards to their relevance to capability engineering e.g. the search and rescue contribute
to the overall crisis management capability but on its own it is very specific. Consequently,
this may influence the information resources to be used to extract the terms or concepts.
These programmes are perceived as capabilities; QEC is perceived as equipment-based,
ATTAC (Availability Transformation: Tornado Aircraft Contract) as service and support
related and GVA (Generic Vehicle Architecture) as capability systems engineering.
1
Note material associated with QEC and Carrier Strike in this paper are limited to that available in the public
domain.

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References
More filters

01 Jan 2002
TL;DR: An ontology defines a common vocabulary for researchers who need to share information in a domain that includes machine-interpretable definitions of basic concepts in the domain and relations among them.
Abstract: 1 Why develop an ontology? In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of ArtificialIntelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites (such as on Yahoo!) to categorizations of products for sale and their features (such as on Amazon.com). The WWW Consortium (W3C) is developing the Resource Description Framework (Brickley and Guha 1999), a language for encoding knowledge on Web pages to make it understandable to electronic agents searching for information. The Defense Advanced Research Projects Agency (DARPA), in conjunction with the W3C, is developing DARPA Agent Markup Language (DAML) by extending RDF with more expressive constructs aimed at facilitating agent interaction on the Web (Hendler and McGuinness 2000). Many disciplines now develop standardized ontologies that domain experts can use to share and annotate information in their fields. Medicine, for example, has produced large, standardized, structured vocabularies such as SNOMED (Price and Spackman 2000) and the semantic network of the Unified Medical Language System (Humphreys and Lindberg 1993). Broad general-purpose ontologies are emerging as well. For example, the United Nations Development Program and Dun & Bradstreet combined their efforts to develop the UNSPSC ontology which provides terminology for products and services (www.unspsc.org). An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them. Why would someone want to develop an ontology? Some of the reasons are:

4,660 citations


Journal ArticleDOI
01 Mar 1998
TL;DR: The paradigm shift from a transfer view to a modeling view is discussed and two approaches which considerably shaped research in Knowledge Engineering are described: Role-limiting Methods and Generic Tasks.
Abstract: This paper gives an overview of the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in recent years we describe three modeling frameworks: CommonKADS, MIKE and PROTEGE-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods and ontologies. We conclude by outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.

3,182 citations


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  • ...Generally, an ontology is defined as an explicit specification of a conceptualisation where concepts and their relations are extracted from the real world (Studer et al. 1998; Duan et al. 2009)....

    [...]


Journal ArticleDOI
01 Jul 2003
TL;DR: This paper reviews and compares the main methodologies, tools and languages for building ontologies that have been reported in the literature, as well as the main relationships among them.
Abstract: In this paper we review and compare the main methodologies, tools and languages for building ontologies that have been reported in the literature, as well as the main relationships among them. Ontology technology is nowadays mature enough: many methodologies, tools and languages are already available. The future work in this field should be driven towards the creation of a common integrated workbench for ontology developers to facilitate ontology development, exchange, evaluation, evolution and management, to provide methodological support for these tasks, and translations to and from different ontology languages. This workbench should not be created from scratch, but instead integrating the technology components that are currently available.

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  • ...A variety of methods, methodologies, tools and languages for ontology development have already been analysed by various authors (Corcho et al. 2002; Mizoguchi 2003; Mizoguchi and Kozaki 2009)....

    [...]


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Frequently Asked Questions (1)
Q1. What contributions have the authors mentioned in the paper "An incremental hybridisation of heterogeneous case studies to develop an ontology for capability engineering" ?

This paper is a continuation of this study led by the CWG ontology work stream that aims to develop a single shared ontology for the concept of capability engineering to enable semantic interoperability and to support a formal and explicit specification of a shared conceptualisation.