Structural Engineering Support Using Semantic Computing

A STRUCTURAL ENGINEERING SUPPORT SYSTEM
USING SEMANTIC COMPUTING

by

Thiti Vacharasintopchai

A dissertation submitted in partial fulﬁllment of the requirements for the
degree of Doctor of Engineering in Structural Engineering

Examination Commitee: Professor Vilas Wuwongse (Chairperson)
Professor Worsak Kanok-Nukulchai (Co-chairperson)
Dr. Pennung Warnitchai
Dr. B. H. W. Hadikusumo

External Examiner: Professor Kincho H. Law
Department of Civil and Environmental Engineering
Stanford University
Stanford, CA, U.S.A.

Nationality: Thai
Previous Degrees: B.Eng. (Civil Engineering)
Chulalongkorn University
Bangkok, Thailand
M.Eng. (Structural Engineering)
Asian Institute of Technology
Bangkok, Thailand

Scholarship Donor: RTG Fellowship

Asian Institute of Technology
School of Engineering and Technology
Thailand
December 2007

i

ACKNOWLEDGMENTS

I would like to thank the Royal Thai Government for granting me a
doctoral research fellowship at the Asian Institute of Technology. My pro-
found gratitude is due to Professor Vilas Wuwongse and Professor Worsak
Kanok-Nukulchai, my mentors and advisors who invariably gave highly
valuable guidance, inspirational suggestions, encouragement and support
throughout this research, to Professor Kincho H. Law from Stanford Uni-
versity who gave highly valuable suggestions as the external examiner, and
to Dr. William Barry, former doctoral advisor, master’s thesis advisor and
good friend, who introduced me to the world of scientiﬁc computing. I
would also like to express sincere appreciation for Dr. Pennung Warnitchai
and Dr. B. H. W. Hadikusumo, doctoral committee members, who gave
invaluable guidance and suggestions. I am grateful to Dr. Pannapa Her-
abat for her unfailingly support and to Dr. Wisit U. Pongsa, my former
employer, who gave several opportunities for conferences and professional
training which form the basis of the work in this dissertation. Dr. Vo-
ratas Kachitvichyanukul provided useful comments during the early stages
of this research. Wouter Rosingh was of great help particularly towards
the end. I wish to thank my parents, Chavalert Vacharasintopchai and
Benjawan Chatnarat, my younger brothers, Nithi and Nipith, family mem-
bers, Apsara Narat and Gallissara Agavatpanitch for their love, support
and encouragement. Thanks are due to friends, especially Dr. Dussadee
Satirasetthavee, Dr. Kannapa Pongponrat, Dr. Pongtawat Chippimolchai,
Neelawat Intaraksa and Anisara Pensuk for their selﬂess care and generos-
ity during my study at the Institute. Assistance from colleagues at the
Knowledge Representation Laboratory and the School of Engineering and
Technology, especially Yupa Soodjitporn and Worranuch Chumchat, are
appreciated. I am also grateful to the Asian Development Bank and the
Greater Mekong Subregion Academic Network for funding the knowledge
management part of this research.

ii

ABSTRACT

Practicing structural engineers are often faced with limitations in avail-
able computing tools as well as insufficient access to relevant knowledge for
design projects and engineering jobs. At the launch of a project the im-
mediate tasks invariably involve research, discussions with colleagues and
a series of computations. These tasks are repeated over and over again
until a final design is arrived at. The degree to which these tasks are well
facilitated in an engineering firm helps determine the productivity of its
engineers and thus the firm’s competitiveness in the industry. Semantic
computing technologies such as Semantic Web Services, Weblog, and Dig-
ital Library have substantial, hitherto untapped potential to improve the
productivity of structural engineers. This dissertation proposes a support
system for structural engineering using these semantic computing technolo-
gies. A system architecture and the key software components for it are
specified and prototype systems presented. The support system involves
two frameworks of software infrastructure: first, the Weblog and Digital
Library Framework for Engineering Knowledge Management (Blog+DL),
which uses Weblogs and Digital Libraries as core components of a collab-
orative structural engineering support system; second, the Semantic Web
Services Framework for Computational Mechanics (SWSCM), a method-
ology that unifies and utilizes scattered computing resources. Blog+DL
and SWSCM are complementary methodologies in the engineering process.
Blog+DL relies on SWSCM to integrate computing resources shared by sev-
eral parties. SWSCM can use Blog+DL to deploy and discover shared com-
puting resources, as well as to educate and exchange knowledge, including
peer opinions about underlying theories and user experiences. Two proof of
concept prototype systems were developed. The first system illustrates the
joint application of Blog+DL and SWSCM to build support systems that
facilitate the computationally oriented knowledge management process in
structural engineering. The second system illustrates the full potential of
SWSCM to facilitate a computationally intensive workflow that involves
heterogeneous engineering software components. Blog+DL and SWSCM
combine semantic computing technologies to build computer-aided engi-
neering tools that improve the productivity of individual engineers and
thereby enhance the competitiveness of engineering firms.

Keywords: engineering support systems, knowledge management, productivity,
software interoperability, Semantic Web Services, Semantic Web, Web Services,
Internet, ontologies, artificial intelligence

iii

TABLE OF CONTENTS

Chapter Title Page

Title Page i
Acknowledgments ii
Abstract iii
Table of Contents iv
List of Figures vi
List of Tables vii

1 Introduction 1
1.1 Motivation 1
1.2 Problem Statement 1
1.3 Objectives 2
1.4 Scope and Research Approach 2
1.5 Contributions 3
1.6 Organization 3

2 Literature Review 4
2.1 Knowledge Management 4
2.1.1 Definition and Significance 4
2.1.2 Classification of Knowledge 5
2.1.3 Theory of Organizational Knowledge Creation 5
2.1.4 Knowledge Management Process and Tools 6
2.2 Knowledge Management in Engineering Design Firms 6
2.2.1 DAR Knowledge Management System 7
2.2.2 ADD and Arup Intranet Systems 7
2.3 Weblogs and Digital Libraries 8
2.3.1 Weblogs 8
2.3.2 Digital Libraries 9
2.4 Web Services 10
2.5 The Semantic Web 12
2.5.1 Background 12
2.5.2 How the Semantic Web Works 13
2.6 Semantic Web Services 15
2.6.1 Background 15
2.6.2 How Semantic Web Services Work 16

3 Development of a Support System for Structural En-
gineering 18
3.1 Structural Engineers and Construction Projects 18
3.2 Design Principles 18
3.3 General System Architecture 20
3.4 Summary 21

4 A Structural Engineering Support System Using
Semantic Computing 22
4.1 Introduction 22

iv

4.2 Blog+DL Framework for Engineering Knowledge Man-
agement 22
4.2.1 Technology Application Framework 22
4.2.2 System Architecture 24
4.3 A Semantic Computing-Based Support System for Struc-
tural Engineers 27
4.3.1 Implementation 27
4.3.2 A Proof of Concept Prototype 29
4.4 Summary 31

5 Engineering Software Interoperability 44
5.1 Introduction 44
5.2 A Semantic Web Services Framework for Computa-
tional Mechanics 44
5.2.1 SWSCM Model 45
5.2.2 Components of SWSCM Service 46
5.2.3 Communication between SWSCM Services 47
5.2.4 Inference Engine and Reasoning Process 48
5.2.5 Matchmaking Algorithm 50
5.3 Application to Structural Analysis and Design 51
5.3.1 Implementation 51
5.3.2 A Proof of Concept Prototype 53
5.4 Summary 56

6 Conclusions 59
6.1 Conclusion and Discussion 59
6.2 Recommendations for Further Research 61

References 63

v

LIST OF FIGURES

Figure Title Page

2.1 Conceptual Web Services Model 10
2.2 Example of RDF Metadata Embedded in Web Page 14
2.3 Sample RDF Statements about Person 14
2.4 OWL-S Service Profile Description of GetSineValue Web Service 17

3.1 General Architecture of Support Systems 20

4.1 Blog, Digital Library, and Knowledge Conversion Modes 23
4.2 Blog+DL Framework for Computer-Aided Engineering Design 25
4.3 User-specific Initialization of Prototype 30
4.4 Blog Publication Features of Prototype 33
4.5 Blog Consumption Features of Prototype 35
4.6 Blog Socialization Features of Prototype 37
4.7 Knowledge Archiving Features of Prototype 38
4.8 Knowledge Retrieval Features of Prototype 41

5.1 Semantic Web Services Model 45
5.2 Semantic SOAP Message 46
5.3 Components of Semantic Web Service 46
5.4 System Configuration of Prototype 52
5.5 Windows Mobile User Interface 54
5.6 Sample Request Message from PDA Client 55
5.7 OWL Definition of ASTM A36 Steel 56
5.8 OWL Definition of Pinned Node 57
5.9 Output Screen on PDA Client 57

vi

LIST OF TABLES

Table Title Page

6.1 Features Comparison with Existing Work 60

vii

CHAPTER 1
INTRODUCTION

1.1 Motivation
Two obstacles that practicing structural engineers often face in a design project
are limitations in available computing tools and limited access to relevant knowledge.
Several tasks are involved when a project is launched, in terms of research, discussion
with colleagues and computational workflow. These tasks are repeated over and over
again until a final design is arrived at. If they are not well facilitated, multiple bottle-
necks will occur and an engineering firm suffers low productivity among its engineers.
Not all engineering firms can afford expensive design software packages, and
the software itself often does not fully address their needs. Some firms may share a
commercial software package or use it on a pay-per-use basis; whereas others develop
software or subroutines in-house. Some in-house tools may be so well-developed or
have such well-developed components that it is more productive for engineers to reuse
them rather than to develop new systems from scratch. However, both commercial
and in-house tools are typically based on specific assumptions that need to be satisfied
beforehand, and their input and output data are usually based on specific conventions
and formats that must be strictly followed to obtain sensible results. As the number
of shared tools grows, it becomes difficult for users to find appropriate tools that fit
their design problems best, and it is tedious for them to manually adapt the potentially
incompatible input and output data of the tools to a computational workflow, such as
the discretization–analysis–postprocessing process in finite element methods.
In terms of knowledge management, on the launch of a new project, file cabi-
nets are often searched to retrieve reference documents of similar past projects. For
less common projects, significant time is typically invested in research to come up with
a solution strategy with the assistance of senior engineers or experts. This process
is time consuming and can become a bottleneck if not well facilitated. Many knowl-
edge management solutions are available in the literature or as commercial software
packages. However, most of them aim single-sidedly to capture tacit knowledge from
experts into a knowledge repository, rather than facilitating the sharing, discussing
and developing of new knowledge among individuals, which are key practices for the
sustainable improvement of knowledge in an organization according to the theory of
organizational knowledge creation (Nonaka and Takeuchi, 1995).

1.2 Problem Statement
This research aims to create a support system that improves the productivity of
structural engineers during the engineering process and, particularly, to alleviate the
two obstacles introduced earlier which hinder the performance of practicing engineers.
The realization of a support system that alleviates productivity obstacles involves ad-
dressing issues in the following areas:

1. Identification of the necessary features for a structural engineering support sys-
tem;
2. Knowledge management best practice according to knowledge management the-
ory; and
3. Effective methodologies for precise storage and retrieval of knowledge.

1

4. Development and sharing of in-house software tools as well as general-purpose
software packages;

5. Automated integration and execution of in-house and general-purpose software
tools;

Knowledge and tools in structural engineering as well as well-established com-
puter technologies, research, and standards are integrated in this study. The former are
required to ensure that the obstacles specific to structural engineering are addressed
and the latter are required to ensure the scalability and sustainability of the proposed
solution.

1.3 Objectives
The primary objective of this study is to develop a support system that improves
the productivity of structural engineers during the engineering process. To fulfill this
primary objective, the following secondary objectives are set:

1. To develop a framework and a prototype support system for structural engineers
that facilitates the improvement and mutual sharing of knowledge and skills in
the form of multimedia documents and computational tools. The framework
includes

(a) the identification of the features of a good knowledge management system
based on knowledge management best practice,
(b) a methodology to appropriately apply computing and information technolo-
gies in the knowledge management context, and
(c) methods and enabling modules that allow knowledge and computational
tools to be created, shared and improved by semantic computing technolo-
gies.

2. To develop a framework and a prototype system that enables the dissemina-
tion and use of intelligently unified computational tools shared in the structural
engineering knowledge system. Specifically, these include

(a) a procedure for structural engineers to develop and/or disseminate in-house
and commercial computational tools,
(b) the data model and format requirements for structural engineering compu-
tational tools to interoperate,
(c) the software services and components necessary to receive, interpret, and
process client requests, and
(d) the decision processes, knowledge, and rules by which a software agent
searches, evaluates, selects, and executes computational tools in a fully au-
tomated structural engineering workflow.

1.4 Scope and Research Approach
The scope of this study covers both knowledge management and computational
interoperability in the structural engineering context. Beyond the literature review

2

traditional for research of this type, the work is anchored in observations of real world
constraints on practicing engineers in engineering consulting firms as experienced by
the author. This led to the development of a Web-based proof of concept knowl-
edge management prototype as well as a set of software infrastructure for the sharing
and interoperability of computational tools. The former facilitates the creating and
sharing of knowledge in the form of multimedia files and computational services. It
allows users to exchange opinions about posted issues and services and to save some
of them to personal knowledge portfolios for later reference. It also allows access to
shared computational tools which can be invoked and interoperated by the software
infrastructure developed in this study. To demonstrate the full potential of this infras-
tructure, a prototype is separately developed to illustrate an intelligent collaboration
of many structural analysis software agents located on personal computers and parallel
computer clusters on the Internet to solve problems in linear elasticity.
The implementation of the system prototypes is based primarily on open-source
software tools. Several widely-accepted semantic computing standards, such as XML,
Web Services and Semantic Web standards sanctioned by the World Wide Web Con-
sortium (W3C), have been used during the development of the frameworks and the
prototypes. As the term “semantic” implies, artificial intelligence techniques are used
extensively.

1.5 Contributions
This study is aimed at improving the productivity of structural engineers in
the engineering process by dealing with computational workflow and knowledge man-
agement issues using semantic computing. The results of this study are an intelligent
structural engineering support system and two underlying frameworks: one which cre-
ates a collaborative environment where structural engineers, both novice and expert,
can share and sustainably improve their knowledge and another which turns the Inter-
net into a large platform for numerical analysis of structures where computational tools
individually developed and located on heterogeneous computer platforms intelligently
collaborate. It is expected that the application of the frameworks and support system
developed and demonstrated in this study can alleviate computational and knowledge
management problems in project design and other engineering work and thus improve
the productivity of individual engineers and the competitiveness of engineering firms.

1.6 Organization
The rest of this dissertation is organized as follows: Chapter 2 gives background
information about the theory, technologies and research that pertain to the develop-
ment of support systems for the engineering process. Chapter 3 describes common
obstacles in the structural engineering process, identifies necessary features and con-
ceptualizes the general design for structural engineering support systems. Chapter 4
presents the structural engineering support system using semantic computing and de-
scribes the architectural framework on which it is based. Chapter 5 addresses the
interoperability of computing resources which enables the sharing of computational
tools via support systems. It proposes a framework for the interoperation of structural
engineering software modules and demonstrates the framework by a system prototype
and a case study. Chapter 6 concludes this dissertation and discusses the benefits and
limitations of the presented work, with recommendations for further research.

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CHAPTER 2
LITERATURE REVIEW

“Semantic Computing encompasses all aspects of computing where data is encoded,
processed, stored or transferred using techniques that communicate the meaning of the
data in addition to the data itself” (Boakes, 2007). An overview of the theory, technolo-
gies and research that pertain to the development of a support system for structural
engineering processes using semantic computing is presented in this chapter. First, the
theoretical background of knowledge management (KM) and two illustrative KM sys-
tems for engineering design firms are presented. Weblogs, a socialization and knowledge
sharing enabling technology for the Web, and Digital Libraries, a technology for fast
and accurate archiving and retrieval of electronic documents and multimedia content
in organizations, are discussed next. Web Services technology, a modern paradigm for
distributed computing on the Internet is then presented. Finally, the Semantic Web, an
emerging technology that brings artificial intelligence to the Web, and Semantic Web
Services, the joint application of Web Services and the Semantic Web, are discussed.

2.1 Knowledge Management
2.1.1 Definition and Significance
Knowledge management is a process for optimizing the effective application of
intellectual assets to achieve organizational objectives (Pollock, 2001). It is “the disci-
pline of creating a thriving work and learning environment that fosters the continuous
creation, aggregation, use and re-use of both organizational and personal knowledge in
the pursuit of new business value” (Cross, 1998, as cited in Quintas 2005). Knowledge,
the sets of processed information in a relevant context ready for understanding and
action (Turban and Aronson, 2001, as cited in Mezher et al. 2005), is an organization-
specific resource that is indispensable to create value for the organization (de Geytere,
2007). Knowledge differs from data and information in that it is “a function of a
particular stance, perspective, or intention” and involves an action “to some end;”
whereas information and data are the “necessary medium or materials for eliciting and
constructing knowledge.” Knowledge, data, and information however are alike in that
they are “context specific” and “relational” (Nonaka and Takeuchi, 1995, p. 58). In
decision making terms, knowledge contains the least “noise” whereas data contains the
most noise pertaining to a decision making process (Sheehan et al., 2005). Business
organizations that manage the creation and the application of knowledge more effec-
tively will be more competitive than others. Organizations that better manage and
share knowledge from past experiences of their peers will more effectively utilize their
resources to fulfill their mission. In the construction industry, which is very competi-
tive with low profit margins, although each project is invariably to some extent unique,
there are common processes which require engineers to seek out knowledgeable persons
“who know what” and to learn the “lessons learned” from them in a timely fashion
to minimize cost and stay competitive. This competitive environment has made KM
attractive in engineering consultant firms (Carrillo and Chinowsky, 2006; Mezher et al.,
2005). KM helps an engineering consulting firm to leverage their technical skills and
store them in a manner to speed up the work, and compete with others in such a way
that projects are produced with high quality in less time (Mezher et al., 2005). As the
world enters the “knowledge society,” the basic economic resource is no longer capital,

4

natural resources, or labor, but is and will be knowledge; and “knowledge workers”
will play an increasingly central role in society (Drucker, 1993; Nonaka and Takeuchi,
1995).

2.1.2 Classification of Knowledge
Knowledge can be classified into two kinds, namely, explicit knowledge and tacit
knowledge. Explicit knowledge can be articulated in formal language such as grammat-
ical statements, mathematical expressions, and manuals, which can be processed by a
computer with relative ease. Tacit knowledge on the other hand consists of personal
knowledge embedded in individual experience and involves intangible factors such as
personal beliefs, perspectives, and value systems, which are difficult to articulate com-
pletely in formal language. Because of its subjective and intuitive nature, it is difficult
to process or transmit acquired tacit knowledge in a systematic or logical manner (Non-
aka and Takeuchi, 1995). A large portion of human knowledge resides in the form of
tacit knowledge, and often tacit knowledge is regarded as the more important kind of
knowledge (Nonaka and Takeuchi, 1995, p. viii), as may be illustrated by the quotes
“If NASA wanted to go to the moon again, it would have to start from scratch, hav-
ing lost not the data, but the human expertise that took it there last time” (Brown
and Duguid, 2000, as cited in Quintas 2005) and “A master craftsman ... develops a
wealth of expertise at his fingertips after years of experience. But he is often unable
to articulate the scientific or technical principles behind what he knows” (Nonaka and
Takeuchi, 1995, p. 8).

2.1.3 Theory of Organizational Knowledge Creation
Knowledge is dynamic in nature. Nonaka and Takeuchi (1995) proposed the
theory of organizational knowledge creation which postulates four temporal modes of
conversion among tacit and explicit knowledge, namely, (1) socialization from tacit
knowledge to tacit knowledge, (2) externalization from tacit knowledge to explicit
knowledge, (3) combination from explicit knowledge to explicit knowledge, and (4) in-
ternalization from explicit to tacit knowledge, which constitute the “knowledge spiral”
that drives innovation in an organization.
Socialization, the process of “sharing experiences and thereby creating tacit
knowledge” such as shared mental models and technical skills, helps an individual
to acquire tacit knowledge directly from others through observation, imitation, and
practice in a field of interaction. Externalization, the process of “articulating tacit
knowledge into explicit concepts,” is the definitive process through which an individ-
ual attempts to conceptualize a mental image linguistically by metaphors, analogies,
concepts, hypotheses, or models. It is typically used in the process of concept cre-
ations and is “triggered” by meaningful dialog or collective reflection. Combination,
the process of “systemizing concepts into a knowledge system,” involves “networking”
different bodies of explicit knowledge in media such as documents or computerized
communication channels so that they are “crystallized” into a new piece of knowledge,
product, service, or methodology. Formal education and training in schools usually
takes the combination form of knowledge conversion. Internalization, the process of
“embodying explicit knowledge into tacit knowledge,” is the process through which
experiences of individuals, obtained through socialization, externalization, and combi-
nation, are internalized into their tacit knowledge bases in the form of shared mental
models or technical know-how. Internalization is closely related to “learning by do-

5

ing.” Documentation helps individuals to internalize what they experienced and also
facilitates the transfer of explicit knowledge to other people.
Each mode of knowledge conversion naturally produces different knowledge
types: socialization produces sympathized knowledge; externalization produces con-
ceptual knowledge; combination produces systemic knowledge; and internalization pro-
duces operational knowledge. These knowledge types interact with each other in a spiral
fashion: sympathized knowledge may become explicit conceptual knowledge through
socialization and externalization. The conceptual knowledge produced then becomes a
guideline for creating systemic knowledge through combination. The systemic knowl-
edge is next converted to operational knowledge through internalization. When an
individual socializes with colleagues and externalizes his knowledge, his operational
knowledge consequently triggers a new cycle of knowledge creation. Knowledge conver-
sion is a social process between individuals and is not confined within an individual. As
human cognition is the deductive process of an individual, who is never isolated from so-
cial interaction when things are perceived, the quality and quantity of tacit and explicit
knowledge are constantly “amplified” through shifts between these social conversion
processes. The four temporal modes of knowledge conversion among individual staff
members, departments, and divisions in an organization therefore constitute a “knowl-
edge spiral” that drives innovation in the organization. This knowledge creation model,
based on the theory of organizational knowledge creation, is sometimes called the
Socialization-Externalization-Combination-Internalization (SECI) model (de Geytere,
2007).

2.1.4 Knowledge Management Process and Tools
The KM process has been discovered rather than invented. Human activity is
inconceivable without knowledge. Even though the phrase “knowledge management”
came into common usage in the mid-1990s, the management of knowledge processes
began long before the term was coined, without the KM label (Quintas, 2005). KM
is different in each organization. There is no one right way and organizations develop
different approaches according to their values and objectives (Sheehan et al., 2005).
Some KM processes that function well may not have the KM label affixed. In contrast
“many so-called KM initiatives and tools that emerged in the late 1990s were less
concerned with addressing real knowledge issues than informal or existing processes
that are not so labeled” (Quintas, 2005). The focus of these initiatives and tools
was often on capturing explicit knowledge, rather than facilitating the conversion and
sharing of tacit and explicit knowledge, as in the SECI model. Their scope can thus
more properly be described as limited to “information management.”

2.2 Knowledge Management in Engineering Design Firms
Structural engineers often work for engineering design and consultant firms
rather than for construction managers or contractors. The nature of their work differs
from that in construction management, in which project planning and scheduling, re-
source allocation, procurement and document management are of more concern. Multi-
ple KM initiatives and tools for engineering design firms are available in the literature,
cf. Mezher et al. (2005); Jonathan Cohen & Associates (2004); Al-Ghassani et al.
(2005); Carrillo and Chinowsky (2006). Most of them, however, rely on proprietary
technologies that do not conform to Internet standards other than basic HTTP and

6

HTML for Web publication. Cross-system interoperability and advanced applications
of Web technologies, such as the Semantic Web (Berners-Lee et al., 2001) and Web feeds
(Wikipedia, 2007e) cannot therefore be implemented without major modifications of
these approaches. For illustrative purposes, the intranet knowledge management sys-
tems at the consultant firms DAR, ADD Inc. and Ove Arup and Partners are presented.
Weblogs and Digital Libraries, recent Internet technologies which overcome these diffi-
culties and can be integrated to form part of a more effective knowledge management
system, are introduced in the next section.

2.2.1 DAR Knowledge Management System
Mezher et al. (2005) developed a KM system at the DAR consulting firm. The
system was developed based on the KM cycle outlined by Turban and Aronson (2001)
which consists of six processes, namely, (1) the creation of knowledge from design
procedures prepared by engineers, lessons learned from construction sites and known
design hints and shortcuts, (2) the capturing of explicit and tacit knowledge created
by the first process, (3) the refining of knowledge through the review and approval
by group leaders and directors, (4) the storage of useful knowledge in a repository
for access by others in the organization, (5) the management of knowledge to main-
tain relevance, accuracy and currency, and (6) the dissemination of knowledge in a
format useful to users. The system consists of shared folders on the department’s file
server repository at the head office and a Web-based user interface that helps users
to access Word, Excel or AutoCAD documents and relevant e-mails in file server hi-
erarchies. Folders on the file server include design standards and manuals, archives of
completed project drawings and documents, pending project drawings and documents,
department-specific software and worksheets, libraries of AutoCAD shortcut menus
and programs as well as document templates and forms, all of which go through the
six KM cycle processes above. Access to these folders is restricted to relevant personnel.
The system is accessible from branch offices through the company’s intranet.
The system provides engineers with ready access to proper design templates and
procedures, as well as past site lessons. It prevents unnecessary re-work and allows en-
gineers to work both faster and more efficiently. Although the knowledge retrieval
process is assisted by a Web interface, it is not clear whether the knowledge creation
and review workflow and repositorial publication, which are part of the externalization
process, are computerized or automated. Metadata, as well as keyword and full-text
search features, which are information and tools necessary for precise retrieval of knowl-
edge from large knowledge bases or nested folder hierarchies, are not mentioned. The
system is therefore likely to not be scalable. Users will also find it difficult to review,
publish and precisely locate pieces of knowledge as the size of the knowledge base grows.
In addition, the system does not support communication means other than e-mails to
enable peer discussion and collaboration, leaving the socialization aspect of the SECI
model relatively unfacilitated.

2.2.2 ADD and Arup Intranet Systems
Jonathan Cohen & Associates (2004) developed intranet KM systems for ADD
Inc. and Ove Arup and Partners. ADD is a medium-sized multidisciplinary design firm
with 150 employees and three offices in Cambridge, Massachusetts, San Francisco and
Miami; whereas Arup is a very large engineering consultant firm with 5,000 employees
in 60 offices and 40 countries.

7

The ADD intranet is a unified Web portal to internal firm information. ADD
staff are directed to a “today” homepage when they enter the site where they are
presented with updated events, news and announcements stored on a shared calendar
database. From the homepage, they can navigate to information about firm standard
procedures, document templates and CAD details. Meetings can be scheduled using
the calendar function; and pictures and news items can be shared among colleagues
through personal pages assigned to each user. These personal pages help to create a
social network across the three offices. A list of experts within the firm is also posted
on the intranet so that they can be promptly consulted should the need arise.
The Arup intranet is an attempt to create a road map to the knowledge within
the firm, which is so large that it is difficult to keep track of all the expertise it possesses.
The Arup intranet is a central repository of the firm’s expertise that provides firm-
wide access to specifications in Word and PDF formats as well as CAD details with a
database that tracks how specific details have been implemented in projects. Arup’s
engineers can annotate their experience with the details and specifications, such as how
they worked in one project and failed in another, and so capture the feedback loop
between design and implementation. These experience records and the feedback loop
are invaluable information and practice that help distinguish Arup in the marketplace.
Employees of Arup are also allowed to “volunteer” information through personal Web
pages where they can quickly and easily share their interests and expertise with the
rest of the firm (Sheehan et al., 2005). In this way individuals can socialize and join
the SECI knowledge spiral which drives innovation at the firm.
The ADD and Arup intranet systems are examples of systems that conform to
KM best practice but rely on proprietary techniques. Cross-system interoperability
and advanced applications of recent Web technologies, which can further improve the
productivity of engineers, cannot thus be implemented easily.

2.3 Weblogs and Digital Libraries
2.3.1 Weblogs
A weblog, or a blog, is a Web-based journal publication which consists primarily
of periodic articles, normally in reverse chronological order (Wikipedia, 2007b). Each
article in a blog is called a blog entry. A person who publishes to a blog is called a
blogger . To publish to a blog is called to blog, the gerund form of which is blogging.
The totality of all blogs on the Internet is called the blogosphere (Wikipedia, 2007c;
Crystal, 2006).
A blog entry usually consists of (1) the title, (2) the content, (3) the categories,
tag names, or keywords of the content, (4) the date and time of publication, (5) the
comment section where readers and the blogger can discuss and exchange experiences
and opinions about issues raised in the blog entry, and (6) the trackback (Trott and
Trott, 2002) hyperlink where the readers who blog about the blog entry can notify
the original blogger about the existence and the content of their blogs. Hyperlinks,
comments, trackbacks, and citations in blogs create online social networks and promote
the externalization, socialization, and combinations of knowledge among the bloggers,
according to the SECI model.
Blogging is as easy as composing an e-mail. A blogger can blog about anything,
ranging from news items, personal opinions, pictures, hobbies, problems at work, to
random thoughts. A blog entry usually contains some hyperlinks to other Web docu-

8

ments which are the subjects of discussion. To publish a blog entry, a blogger logs on
to his account on the blog service provider Website, cf. Blogger (2007) and WordPress
(2007), clicks the compose button, types the content into a Web form, enters the title
and keywords for the content. Multimedia contents such as pictures, audio/video clips,
as well as Word and Excel files can be uploaded and included as attachments. When a
publish button is clicked, the blog entry is instantly published to the Internet and can
be accessed by friends, colleagues, and the public through regular Web browsers, Web
search engines, and dedicated blog reader software which conforms to the Really Sim-
ple Syndication (RSS) (RSS Advisory Board, 2006) or Atom (Nottingham and Sayre,
2005) Web syndication (a.k.a. Web feed) standards. A blogger can also send e-mails
to a secret e-mail address to publish the contents and picture attachments directly
from an e-mail client software on a laptop computer or a mobile phone. Blogging thus
allows people to share information on the Internet with very few barriers. Technorati,
Inc., a Web tracking company, reported that as of April 2007 there were 70 millions
blogs on the Internet, with 120,000 new blogs added each day, and 1.5 million posts per
day. The blogosphere grew from 35 to 70 million blogs in 320 days. Japanese, English,
Chinese, and Italian are the four major blogging languages—37 percent of the blogs
are in Japanese, 33 percent in English, 8 percent in Chinese, and 3 percent in Italian
(Sifry, 2007).

2.3.2 Digital Libraries
A digital library is an integrated set of services for capturing, cataloging, stor-
ing, searching, protecting, and retrieving information (Reddy et al., 1999). It comprises
focused collections of digital objects, including text, video, and audio, along with meth-
ods for access and retrieval, and for selection, organization, and maintenance of the
collections (Witten and Bainbridge, 2003), all of which support life-long learning, re-
search, scholarly communication and preservation (Wikipedia, 2007d). Digital libraries
were originally used to archive the digitized copies of rare documents, books, and the
pictures of historical objects so that they can be studied by people of later generations.
They have also recently been used as central repositories to preserve the works of in-
dividuals in an organization, so that they do not vanish with time and technological
obsolescence (DSpace, 2007b).
A digital library system is often developed such that it is accessible on the
Web, with the user interface resembling that of a Website. However, not all Websites,
even the ones that offer focused collections of well-organized material and appropriate
methods of access and retrieval, can be regarded as digital libraries, unless metadata
(data about data), which are important information to precisely catalog, locate and
retrieve pieces of information, are stored along with each object in the collections,
and the access, retrieval, and modification of metadata, as well as the retrieval of
objects based on them, are facilitated by the system (Witten and Bainbridge, 2003).
The metadata typically used in digital libraries include bibliographic information and
subject keywords, much as the indexing information used in physical libraries.
The users of a digital library system have one of two roles, namely, the publisher
and the reader. In some digital library implementations, such as the Greenstone Dig-
ital Library software (Witten et al., 2006), a librarian takes the sole publisher role to
capture (e.g., by scanning printed articles or typing in a word-processor), create cata-
loging metadata, store, manage, and publish the collections of digital objects; and the
general users take the reader role to browse, search, and consume information in the

9

Service
UDDI
Description
Service
WSDL
Registry

Pu
d

bli
Fin

sh
Service Service
Bind
Consumer Provider
SOAP Web Service

Figure 2.1: Conceptual Web Services Model

collections. In other digital library implementations, such as DSpace (MIT Libraries
and Hewlett-Packard Company, 2007) which is based on an institutional repository
concept, select users may also publish to some collections in the repository, thus taking
both the publisher and the reader roles. Computers and users can locate objects across
heterogeneous digital library systems by using standard query protocols, such as the
Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) (Lagoze et al.,
2004). When a digital library system is deployed in an organization, it functions as a
knowledge base which stores explicit and externalized knowledge from the users. The
metadata-rich search feature of the system also allows users to precisely and promptly
locate pieces of knowledge which can be used to solve their specific problems in a timely
fashion. In this sense, a digital library system can thus promote the combination and
internalization of knowledge in the SECI model.

2.4 Web Services
Web Services (W3C, 2004d) is a modern paradigm of distributed computing
that uses the Internet as the communication medium. Its fundamental concept is to
build computer software by making use of remote procedure calls (RPC) over the In-
ternet or an intranet. It differs from other distributed computing technologies, for
example CORBA (OMG, 2005) and DCOM (Microsoft, 1996), in the use of platform-
independent standards, based on HTTP (Fielding et al., 1999) and Extensible Markup
Language (XML) (Fallside, 2001), which allows service providers to hide the imple-
mentation details from clients. The clients need to know the URLs (Berners-Lee et al.,
1994) of the services and the data types for method calls, but not the details of the
service implementation in order to use them (Basha et al., 2002). Web Services is dif-
ferent from the common term “web services” (spelled in lower case) adopted by some
researchers, which simply refers to the “ability to access many services through the
Internet” (Chen et al., 2006) via an arbitrary communication protocol without regard
to the specific World Wide Web Consortium (W3C) standards that are introduced in
this section.
The typical architecture of Web Services is shown in Figure 2.1. Three roles,
namely, a service provider, a service consumer, and a service registry, and three oper-
ations, namely, publishing, finding, and binding, are involved.
A service provider is an entity that creates Web services. Typically, it exposes

10

certain functionality in their organization as a Web service that is made available for
any other organization to invoke. To be effective, a Web service needs to perform
two tasks. First, it needs to describe the Web service in a standard format that
is understandable by all organizations that will be using that Web service. Next it
needs to publish the details about its Web service in a central registry that is publicly
accessible.
A service consumer is any organization that uses Web services provided by
a service provider. It knows the functionality of a particular Web service from the
description made available by the service provider. To retrieve those details, a service
consumer searches the registry where the service provider has published its Web service
description. The service consumer can then obtain the description of the required
mechanism, bind to the service provider’s Web service and invoke that Web service.
A service registry is a central location where service providers list their Web
services, and where service consumers search for them. Service providers usually pub-
lish their Web service capabilities in the service registry for service consumers to find
and later, if interested in the provided service, bind to them. Examples of information
typically stored in the service registry include the detailed description of an organiza-
tion, the Web services that it provides, and a written description of each Web service
as well as the input and output formats for its invocation.
Web services take multiple roles on different occasions. A Web service takes
the “provider” role when it accepts requests from consumers to be processed. It takes
the “consumer” role when it delegates its tasks to other Web services. A Web service
provider takes the special role of “service registry” if it helps a service consumer to
discover a service provider for later binding. A client program that interacts only
with end users takes the consumer role when it requests a service from a Web service
provider.
The Web Services architecture aims to achieve interapplication communication,
irrespective of the programming language that the application is written in or the
platform that the application is running on, by three fundamental operations which
are “finding,” “binding,” and “publishing.” To make this happen, standards for each
of these three operations and a standard way for a service provider to describe their
Web services are needed.
The Web Service Description Language (WSDL) (W3C, 2001) is such a standard
that uses the XML data format to describe Web services. The WSDL document for a
Web service defines the methods that are present in the Web service, the input/output
parameters for each method, the data types, the network transport protocol used, and
the URLs where requests for services are to be submitted and from which the results
can be received.
The Universal Description, Discovery, and Integration (UDDI) standard (OA-
SIS, 2002) provides a way for service providers to publish details about their organi-
zation and the Web services that they provide to a central registry. It also provides a
standard for service consumers to find service providers and details about their Web
services. Publication of the details is the “description” part of UDDI and the location
of such details is the “discovery” part of it. Publication of these details is typically
achieved by advertising hyperlinks to their WSDL documents on UDDI service reg-
istries.
Communication between service consumers, service providers and service reg-
istries is through the Simple Object Access Protocol (SOAP) (W3C, 2000), which is a

11

lightweight XML communication mechanism to exchange information between appli-
cations, regardless of the operating systems, programming languages, or object models
employed in their development. SOAP may be visualized as a “carrier” of data encoded
in XML format.
Web Services technology has been involved in many areas of scientific comput-
ing, ranging from computational infrastructure developments (Chiu et al., 2002; van
Engelen, 2003) to finite-element analysis of a coupled fluid, thermal, and mechani-
cal fracture problem (Chew et al., 2003). One significant effort in this area is the
standardization attempt by the Global Grid Forum (GGF, 2003) to create the Open
Grid Service Architecture specification (OGSA) (Foster and Gannon, 2003) which is
the global standard for interoperation in the grid computing community (Foster, 2002;
Foster et al., 2001) and is the specification in which the SOAP and WSDL standards,
two important components of Web Services architecture, have been adopted. As there
are already a significant number of scientific computing projects that rely on grid com-
puting infrastructure, such as NASA’s Information Power Grid (IPG, 2003) and the
TeraGrid (Catlett, 2002), imposition of such a standard is likely to increase the number
of scientific computing applications that rely on Web Services technology significantly.

2.5 The Semantic Web
2.5.1 Background
The Semantic Web (Berners-Lee et al., 2001) is a vision for the next generation
of the Web on which information will be useful and meaningful not only for people
but also for computers. Computers will be able to understand pieces of information
on Web pages rather than merely presenting them to users, and will thus be able to
autonomously assist users in manipulating the information.
The Semantic Web is not a separate Web. Rather, it is an evolution of the
present Web into one in which information is given well-defined meaning (Berners-Lee
et al., 2001). Documents on the present Web can be transformed into Semantic Web
documents by augmenting them with metadata aimed at computers. Metadata is data
about other data that enables computers to determine the meaning of information
in a Web document by following hyperlinks to definitions of key terms and rules for
reasoning about them logically.
The Semantic Web relies on two existing technologies which are XML and the
Resource Description Framework (RDF) (Berners-Lee et al., 2001).
XML (Fallside, 2001), a generalization of HTML (W3C, 1999a), has been pro-
posed as the language for the publication of Web documents. XML allows Web pub-
lishers to add structure to their documents by using tags, which are hidden labels that
annotate Web pages or sections of text on a page. Computer programs can make use of
these tags in many ways, from rendering XML documents in a human-friendly format
on Web browsers, to mapping XML documents as data sources. However, XML tags
are not uniformly created and they can be used by different groups of people. There-
fore, their intended meaning must be precisely comprehended by the programmers in
order to develop programs that properly manipulate the XML tags. The intended
meaning of an XML tag will be understood if the name of the tag is drawn from a
common dictionary.
RDF (W3C, 1999b) is a language for describing information about resources
on the Web. Typically encoded in XML format itself, the RDF language allows Web

12

publishers to assert that particular things (Web pages, videos, etc.) have properties
(such as “is authored by” or “is a kind of”) with certain values (such as a thing,
a person, etc.), in a grammatical form similar to the subject, verb, and object of
an elementary sentence (Berners-Lee et al., 2001). An assertion that the Web page
“www.asce.org” “is authored by” “ASCE” is called an RDF statement, or a triple. A
set of triples with the same term or concept in a domain forms a definition of the term.
A collection of the definitions and the relations among terms in a domain constitutes
an ontology of the domain. In the context of the Semantic Web, an ontology is a
document or file that formally defines terms and their relations (Berners-Lee et al.,
2001). It can serve as a common dictionary from which XML tag names, and RDF
property names and values can be drawn.
Subjects, verbs, and objects in RDF statements are uniquely identified by Uni-
versal Resource Identifiers (URIs). The Uniform Resource Locators (URLs) that link
Web pages are the most common type of URI. URIs ensure that terms or concepts are
not just words in a document but are tied to a unique definition that anyone can find
on the Web (Berners-Lee et al., 2001). In addition, the object of an RDF statement
can be another RDF statement, so that complex RDF statements can have other RDF
statements nested inside in multiple layers. Figure 2.2 illustrates the RDF statements
serving as metadata about the Web page http://ce-res.blogspot.com/2005/10/
kmitlsut-reinforced-concrete-design.html. It asserts that the page “has title”
“Reinforced Concrete Design Worksheets,” is categorized in the “subjects” “RC De-
sign,” “Working Stress Design,” etc., and “is authored by” the person identified by the
URI http://stweb.ait.ac.th/~ st029284/foaf.rdf#thitiv.
Ontologies and RDF statements are typically stored on Web servers for later
retrieval by other parties in the same way as the publication of traditional Web doc-
uments. The statements about the author of the Reinforced Concrete Design Work-
sheet can be retrieved for display on a Web browser by accessing the URI http:
//stweb.ait.ac.th/~ st029284/foaf.rdf#thitiv. It is also possible to embed an
RDF statement that employs terms from specific ontologies inside hidden tags of Web
documents themselves (Palmer, 2002). Software tools such as the Jena Semantic Web
Framework (Jena, 2007) provide a means to retrieve ontologies and RDF statements
from Web sources and store them in a local knowledge base. The set of inference
rules that correspond to the semantics of the RDF constructs (W3C, 2004c) is prebuilt
into Jena. Answers to queries such as “give me a list of Web pages authored by
http://stweb.ait.ac.th/~ st029284/foaf.rdf#thitiv” can be obtained through
Jena’s Application Programming Interfaces (APIs) (Wikipedia, 2007a).

2.5.2 How the Semantic Web Works
The following examples illustrate how Web documents, XML, ontologies, and
RDF statements work on the Semantic Web.
Example 1
John Doe is at the Reinforced Concrete Design Worksheet Web page on the CE
Resources Website (Vacharasintopchai, 2005). He is interested in the link mentioned on
the page and wants to contact the author for more information about it. The author’s
e-mail address or contact information is not given on the Web page. How can John
find the author’s e-mail address?

Traditional Solution. On the Web page, John has to find the name of the author.

13

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:dc="http://purl.org/dc/elements/1.1/">
< rdf:Description
rdf:about="http://ce-res.blogspot.com/2005/10/kmitlsut-reinforced-concrete-design.html"
5 dc:title="Reinforced Concrete Design Worksheets">
<dc:subject rdf:resource="http://thitiv.blogspot.com/semblog/terms.owl#rc-design"/>
<dc:subject rdf:resource="http://thitiv.blogspot.com/semblog/terms.owl#working-stress-design"/>
<dc:subject rdf:resource="http://thitiv.blogspot.com/semblog/terms.owl#ultimate-strength-design"/>
<dc:subject rdf:resource="http://thitiv.blogspot.com/semblog/terms.owl#structural-members"/>
10 <dc:language rdf:resource="http://www.iso.org/iso/en/prods-services/iso3166ma/02iso-3166-code-
lists/list-en1.html#TH"/>
</ rdf:Description >
</rdf:RDF>
15 xmlns:dc="http://purl.org/dc/elements/1.1/">
< rdf:Description
rdf:about="http://ce-res.blogspot.com/2005/10/kmitlsut-reinforced-concrete-design.html">
<dc:author rdf:resource="http://stweb.ait.ac.th/~st029284/foaf.rdf#thitiv"/>
</ rdf:Description >
20 </rdf:RDF>

Figure 2.2: Example of RDF Metadata Embedded in Web Page

xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xmlns:foaf="http://xmlns.com/foaf/0.1/">

5 <foaf:PersonalProfileDocument
rdf:about="http://stweb.ait.ac.th/~st029284/foaf.rdf">
<foaf:maker rdf:resource="#thitiv"/>
<foaf:primaryTopic rdf:resource="#thitiv"/>
</foaf:PersonalProfileDocument>
10
<foaf:Person rdf:ID="thitiv">
<foaf:givenname>Thiti</foaf:givenname>
<foaf:family_name>Vacharasintopchai</foaf:family_name>
<foaf:mbox rdf:resource="mailto:thitiv[at]gmail.com"/>
15 <foaf:homepage rdf:resource="http://thitiv.blogspot.com"/>
</foaf:Person>

</rdf:RDF>

Figure 2.3: Sample RDF Statements about Person

Then he has to go to a search engine, such as Google, and type in the author’s
name and a keyword such as “e-mail” to search for traces of an e-mail address.
If John is lucky enough, he gets the e-mail address after the ﬁrst search.

Semantic Web Solution. A set of RDF statements that describe the Web page (Fig-
ure 2.2) will usually be published in conjunction with the page content. A Se-
mantic Web-enabled Web browser will recognize the “is authored by” RDF state-
ment (the dc:author and the rdf:resource tags) and will be directed to the
RDF statements about the author. The Web browser can inspect the statements
about the author (Figure 2.3) and extract the author’s e-mail address from the
foaf:mbox statement automatically. This e-mail address will be suggested by
the Web browser as the e-mail address that John is looking for.

14

Example 2
The RC design Web page is categorized on the Website under a keyword “struc-
tural members.” If John wants to see more articles about “structural members” from
the same author, how would he do it?

Traditional Solution. On the Web page, the author provides a link to an index of
articles in the “structural members” category. If John clicks on the “structural
members” link, he is directed to a list of articles about “structural members.”
Had the author not provided such an index, John would have to search for a list
of articles with “structural members” and the author’s name as the keywords.
However, John is not likely to encounter an article about “beams,” although
obviously it is a kind of structural member, unless “beams” is also explicitly
added to the keyword list.

Semantic Web Solution. The Web browser will recognize the “subject” statements
(the dc:subject tag) in the set of RDF statements about the RC design Web
page (Figure 2.2). When requested to ﬁnd more Web pages whose dc:subject
property is http://thitiv.blogspot.com/semblog/terms.owl#structural-members
and whose dc:author property is http://stweb.ait.ac.th/~ st029284/foaf.
rdf#thitiv, the Web browser can browse through a list of RDF statements,
then select the Web pages whose dc:author and dc:subject are the same as,
or related to, the given ones, and then present the result accordingly. If the Web
browser ﬁnds an RDF statement which asserts that http://thitiv.blogspot.
com/semblog/terms.owl#beams is a “subclass of” (W3C, 2003) http://thitiv.
blogspot.com/semblog/terms.owl#structural-members, it will also include
the article about “beams” in the results, as it can be deduced by an inference
engine that beams are a type of structural member. In technical terms, we say
that the concept “structural members” subsumes the concept “beams.”

It should be noted here that the term “RDF statement,” used earlier, collectively
refers to the statements that are represented by the RDF language (W3C, 2003) as well
as the Web Ontology Language (OWL) (W3C, 2004a), which is an extension of the RDF
language for more comprehensive representation of knowledge on the Semantic Web.
OWL provides several additional constructs, such as “equivalent class,” “intersection
of,” “union of,” “inverse of,” and “disjoint with,” that more precisely capture bodies
of knowledge over those available in the RDF language. Henceforth, the term “OWL
statement” may be used when it is desirable to emphasize the employment of OWL’s
additional constructs in the statement.

2.6 Semantic Web Services
2.6.1 Background
Web Services introduced a new model of distributed computing that uses the
Internet as the communication platform. The present technology based on WSDL,
SOAP, and UDDI allows automatic publication, discovery, and execution of services at
the syntactic level. However, WSDL documents, which play a key role in the interop-
eration of Web services, are XML documents and therefore inherit the same semantic
problem of XML discussed earlier. To make proper use of the Web services advertised
by WSDL documents, programmers have to know in advance the intended meaning

15

of the custom tags that specify the input and output schemas as well as the names of
services that a Web service provides. Like other XML documents, WSDL documents
are usually created and used by different groups of people. Unless supported by on-
tologies, it would be difficult for a computer program looking for a GetSineValue Web
service that calculates the sine of a “degree” angle to autonomously execute and get
the correct sine value from an advertised TrigonometricSine Web service that takes
“radian” angles as the input, even though both of them are described as Web services
that calculate the sine values of angles.
Semantic Web Services, the idea of augmenting Web service descriptions using
Semantic Web technology (Burstein et al., 2005), were introduced to address this kind
of problem and to facilitate the autonomous publication, discovery, and execution of
services at the semantic level. Semantic Web service description languages, such as
OWL-S (OWL–S, 2003) and Web Service Modeling Ontology (WSMO) (Roman et al.,
2005), were proposed as abstractions of syntactic Web service description languages
such as WSDL, which are used in current Web Services technology. They are meant
to be used with semantic matchmakers, which are the software agents that accept and
keep track of the descriptions of available services from providers and match them
against the requirements from service consumers (Burstein et al., 2005). Matchmakers
enhance the role of UDDI service registries in the Semantic Web Services architecture.
OWL-S is among the most widely used semantic Web service description lan-
guages and was submitted to W3C for possible standardization (Martin et al., 2004).
It describes the categories, the inputs, the outputs, and the consequences of Web ser-
vices in terms of concepts defined in OWL ontologies. It also provides the grounding
constructs for specialization into WSDL constructs for compatibility with existing Web
services, which are described by WSDL documents. OWL-S is used as the semantic
Web service description language in this research.

2.6.2 How Semantic Web Services Work
To illustrate how Semantic Web Services can help a program looking for the
GetSineValue Web service to get the correct value of sine from the TrigonometricSine
Web service, consider the OWL-S description of GetSineValue in Figure 2.4. The
“service profile” description, which is the proposed information for use by matchmakers
(Sycara et al., 2003), is presented. Figure 2.4 shows that:

1. The GetSineValueService (which is the service that the program is looking for)
is a kind of SineFunctionEvaluator defined in the profileHierarchy taxon-
omy;

2. The service takes one input parameter “Angular Degree” the definition of which is
available at http://std.swscm.org/200407/quantities.owl#AngularDegree,
and gives one output the definition of which is available at http://std.swscm.
org/200407/quantities.owl#UnaryConstantFunctionQuantity; and

3. The effect of this service is defined by http://std.swscm.org/200407/Math-
OperationEffects.owl#TrigonometricFunctionValueEffect.

In the Semantic Web Services architecture, when a service consumer needs a
service from a provider, it creates an ideal service profile, such as the one in Figure 2.4,
and submits it to a matchmaker as a request for recommendation (Sycara et al., 2003).

16

<rdf:RDF>
<service:Service rdf:ID="GetSineValueService">
<service:presents rdf:resource="#GetSineValueServiceProfile"/>
</service:Service>
5
<profileHierarchy:SineFunctionEvaluator rdf:ID="GetSineValueServiceProfile">
<service:isPresentedBy rdf:resource="#GetSineValueService"/>

<profile:hasInput>
10 <process:Input>
<process:parameterType
rdf:resource ="http://std.swscm.org/200407/quantities.owl#AngularDegree"/>
</process:Input>
</profile:hasInput>
15
<profile:hasEffect rdf:resource=
"http://std.swscm.org/200407/MathOperationEffects.owl#TrigonometricFunctionValueEffect"/>

<profile:hasOutput>
20 <process:UnConditionalOutput>
<process:parameterType
rdf:resource ="http://std.swscm.org/200407/quantities.owl#UnaryConstantFunctionQuantity "/>
</process:UnConditionalOutput >
</profile:hasOutput>
25
</profileHierarchy:SineFunctionEvaluator >
</rdf:RDF>

Figure 2.4: OWL-S Service Profile Description of GetSineValue Web Service

From the profiles registered by various service providers, the matchmaker selects the
one that most closely matches the ideal profile requested, and recommends it to the
consumer for further binding between the two parties.
As the matchmaker processes the request, suppose that it had searched the list
of service profiles advertised by service providers but could not find any service that
matched perfectly. However, there is a TrigonometricSine Web service that advertises
itself as a kind of SineFunctionEvaluator which gives a UnaryConstantFunction-
Quantity output and the TrigonometricFunctionValueEffect effect. The service
takes the input parameter defined by http://www.math.org/terms.owl#RadianAngle
rather than the http://std.swscm.org/200407/quantities.owl#AngularDegree. If
there are some assertions that the “radian angle” and the “angular degree” concepts
are related in some ways, for example:

1. Both “radian angle” and “angular degree” are subclasses of http://www.math.
org/terms.owl#PlaneAngle; and

2. The “angular degree” “has conversion factor” of π/180 and ”has reference unit
of measurement” of http://www.math.org/terms.owl#RadianAngle,

by populating these assertions into the knowledge base of an inference engine, and
by querying whether the pairs of service types, input parameters, output parameters,
and effects, subsume each other (Sycara et al., 2003), the matchmaker will be able to
determine that the two Web services near perfectly match, and could thus recommend
this TrigonometricSine Web service to the consumer. The consumer will be able to
use the “has conversion factor” assertion to convert its angular degree into the radian
angle unit expected by the service provider, and will be able to initiate a request to
the TrigonometricSine Web service and finally get the correct sine function value.

17

CHAPTER 3
DEVELOPMENT OF A SUPPORT SYSTEM FOR
STRUCTURAL ENGINEERING

3.1 Structural Engineers and Construction Projects
Construction projects start when owners decide to have buildings constructed to
satisfy their needs. Through bidding processes or fast track approaches, an architect is
contracted, the owner’s requirements collected and a preliminary design agreed upon.
A series of drawings and specifications which consist primarily of architectural and
structural work are indispensable for the materialization of a project. Tender drawings
and specifications are required for bidding to select a general contractor for the project.
Detailed drawings from designers and shop drawings from field engineers are necessary
for work at the construction site to proceed.
Structural engineers play a critical role in all these construction project phases.
At the outset, architects need a structural engineers’ opinion about viable structural
systems that are appropriate for preliminary designs. Structural engineers are con-
sulted for the estimated size of columns, beams and slabs before architectural drawings
are developed for tender. Detailed foundation designs are often the first construction
drawings requested from structural engineers, even though the information for them
is last obtained in the analysis and design process. In addition, field engineers need
prompt advice from structural designers when unexpected difficulties occur at the con-
struction site to minimize expenses from work delays.
Sound structural analysis and design is central to the safety and economy of a
construction project; yet structural engineers are often on the critical path with tight
schedules and are allocated very limited time for their tasks. Two obstacles that hinder
the productivity of structural engineers in dealing with their tasks are limitations in
available computing tools and limited access to relevant knowledge. A support system
that improves the productivity of structural engineers during the engineering process
is therefore highly advantageous, as postulated in the first chapter.

3.2 Design Principles
Structural engineering departments typically have both senior engineers with
extensive practical experience and junior engineers (or fresh graduates) with little pro-
fessional experience. Many kinds of tools are used in the engineering process. Junior
engineers are often more acquainted with relatively new technologies, whereas senior
engineers have a wealth of practical insight and often know useful shortcuts. Some
commercial software packages are licensed and many departments have special-purpose
software tools that have been individually developed in-house. Commercial software
and in-house tools are usually shared among engineers in the department. The output
from structural engineers is commonly in the form of hand sketches or CAD drawings,
supported by calculation sheets. On completion of projects, relevant documents are
collected on file servers or in cabinets for future reference.
To improve the productivity of structural engineers in dealing with their tasks,
good support systems should at least have the following features:

1. References and Premises Engineering is an applied science by nature. It deals
with the application of pure scientific knowledge to solve practical problems.
When structural engineers are assigned tasks at the launch of new projects, ref-

18

erence documents, such as design manuals, theoretical notes and data sheets, as
well as examples from similar past projects, are the first information needed to
come up with solution strategies. Good support systems for structural engineers
should thus facilitate the efficient storage, as well as fast and precise retrieval, of
reference documents and premises to minimize the engineers’ start-up time.

2. Knowledge and Tools Sharing Engineers study project requirements and pre-
dict assumptions to simulate the behavior of structures under critical usage con-
ditions. Based on the mechanical properties of construction materials, structural
members are designed such that they can withstand severe conditions at reason-
able levels of safety. To maximize the safety and economy of structural engineer-
ing products and arrive at proper rather than over- or under-designs engineers
should have access to the best possible set of knowledge and tools relevant to their
tasks. Methodologies, tips and techniques, as well as software tools licensed from
commercial vendors or developed in-house, should be accessible to as many fel-
low colleagues as possible. Good support systems for structural engineers should
second facilitate the creating, sharing and disseminating of knowledge and tools
among structural engineers so that they are well equipped for their work.

3. Computational Workflow Commercial and in-house software tools are typically
based on specific assumptions that need to be satisfied. Their input and output
data are usually based on specific conventions, formats and units of measurement
that must be strictly followed to obtain sensible results. Such software is often
chained into a computational workflow to address specific requirements unsup-
ported by general-purposes software packages. It is tedious and error-prone for
the engineers to adapt the potentially incompatible input and output data of
tools in the chain manually. It is also difficult for users in workplaces where nu-
merous software tools are shared to find the tools that fit their specific problem
best. Good support systems for structural engineers should thus thirdly facilitate
the interoperation of software tools and allow them to be chained such that the
least possible user intervention is required to use them effectively and prevent
blunders.

4. Discussion and Collaboration Engineering is collaborative in nature. Senior
engineers in structural engineering departments have a wealth of practical insight
and experience, whereas junior engineers have fewer but are more acquainted
with modern technologies and design techniques. In general, the two exchange
knowledge and learn from each other. Senior engineers give guidelines and opin-
ions about approaches to a solution. Colleagues give suggestions about tools
and techniques appropriate for specific project types. Although members of en-
gineering teams usually meet and discuss formally on a regular basis, human
knowledge is amplified during informal socialization, as postulated by the theory
of organizational knowledge creation in Chapter 2. To assist structural engineers
in exchanging and amplifying their knowledge in other than lunch and hallway
meetings, good support systems for structural engineers should fourthly facilitate
informal discussion and collaboration among peers. The systems should allow ex-
changes of audio/video clips, worksheets, pictures and drawings for effective peer
communication. To encourage user participation, the tool for discussion and col-
laboration should be easy to use. The systems should also have an archiving

19

Figure 3.1: General Architecture of Support Systems

feature to make both past projects and the group thinking processes behind the
solutions arrived at, accessible to colleague engineers in the future.

5. Accessibility and Interoperability Support systems are not useful if they are
difficult to access. They will not wide gain acceptance and adoption in the
industry if they lack interoperability with other systems. In addition to the four
features above, good support systems for structural engineers should therefore
follow open standards and be platform neutral for cross-system interoperability
and accessibility. Users on different computing platforms (such as Windows, Mac
OS and Linux) should be able to access the systems indifferently. Ubiquitous
access to the systems, especially from remote construction sites, would also be
advantageous for timely access to knowledge and tools to assist with unforeseen
problems on-site.

3.3 General System Architecture
The semantic computing technologies reviewed in Chapter 2 provide various
functionalities which can be combined to meet the five requirements for good struc-
tural engineering support systems. This section proposes a general system architecture
which utilizes semantic computing technologies to build such a system. The system
architecture consists of five components as depicted in Figure 3.1. Each component is
labeled with numbers (1), (2), . . . , (5), which correspond to item numbers of the design
principles. For example, (1) refers to References and Premises and (2) refers to Knowl-
edge and Tools Sharing. The relationships between these components and the design
principles can be explained as follows:

Digital Library The need for support systems to facilitate efficient storage and fast,
precise retrieval of premises and reference documents matches well with the meta-
data rich content archiving and retrieval features of digital libraries. Digital li-
braries are specifically developed to store large amounts of electronic documents
for archival purposes and to allow them to be precisely located and retrieved
with relative ease. Digital Library technology is therefore appropriate for the
References and Premises requirement. The Web access and conformance to stan-
dard query protocols of digital library systems make them appropriate for the
Accessibility and Interoperability requirement.

20

Weblogs The need for support systems to facilitate the creating, sharing, and dis-
seminating of knowledge and tools matches well with the publication and social
networking features of Weblogs. Blogging allows engineers to instantly document
and disseminate among peers new ideas, software tools and techniques, and to
conveniently receive focused feedback on particular topics in the form of com-
ments and trackbacks. Engineers can also blog about work plans and difficulties
arising in particular projects to solicit comments and suggestions from colleagues.
Weblogs, in this regard, are therefore appropriate both for the Knowledge and
Tools Sharing and the Discussion and Collaboration requirements. The confor-
mance of Weblog servers to Web feed standards and the convenient means of
publication offered by Weblogs make the technology especially appropriate for
support systems, from the Accessibility and Interoperability point of view. In
addition, the bibliographic information of blog entries is important metadata
which contributes to the fast and precise retrieval of knowledge in blog content,
according to the References and Premises principle.

Web Services The need for support systems to facilitate with the least user inter-
vention the interoperation and chaining of software tools matches well with the
capability of Web Services and Semantic Web Services to help unify and utilize
scattered computing resources. Web service providers and consumers include,
but are not limited to, software tools on parallel computer clusters, personal
computers, laptops and smartphone devices. With premises from service meta-
data (i.e. service descriptions) and ontologies, the artificial intelligence inherent
in Semantic Web Services technology also helps to intelligently discover and unify
heterogeneous computational services and to reduce the necessity for user inter-
vention during workflow processes. Web Services and Semantic Web Services
technologies, in this regard, are thus particularly appropriate for structural engi-
neering support systems, in terms of Computational Workflow, Accessibility and
Interoperability, as well as Reference and Premises.

3.4 Summary
This chapter has described common obstacles in the structural engineering pro-
cess and proposed five principles for the design of support systems to assist engineers
in dealing with their tasks more productively. Semantic computing technologies were
identified as enabling tools for such systems and a general architecture for the develop-
ment of structural engineering support systems has been proposed. The methodology
to create support systems from these technologies are presented in the next two chap-
ters, along with proof of concept prototypes. Metadata and ontologies as well as their
annotation in data and knowledge are central to all aspects of the development, hence
the term “semantic computing.” Their importance is illustrated throughout.

21

CHAPTER 4
A STRUCTURAL ENGINEERING SUPPORT SYSTEM USING
SEMANTIC COMPUTING

4.1 Introduction
Weblogs (a.k.a. Blogs) and Digital Libraries, information technologies deploy-
able on the Internet or an intranet, can be combined to build a new generation of
knowledge management (KM) systems, which can enhance the access to knowledge
and expertise of engineers, as previously postulated. Such systems assist them in
sharing tacit knowledge, developing concepts from many pieces of tacit knowledge,
combining various elements of explicit knowledge, and storing new knowledge in the
knowledge bases of individuals and the organization. In such a KM system, an engineer
is assigned a personal blog where he/she can store work in progress, including collec-
tions of random thoughts, ideas, documents, spreadsheets, or audio-video recordings.
Through the dynamic social networking facilities of blogs, exchanges of comments and
discussions among colleagues are electronically facilitated and individual blog entries
and discussions can be cross-referenced. After a work-in-progress is completed, related
blog entries and discussions, as well as a project summary document, can be archived
to a digital library for future reference.
This chapter builds on the general design for structural engineering support
systems described in Chapter 3 to propose a methodology by which blogs and digital
libraries can be combined into a structural engineering support system based on three
enabling modules which will be described and specified. A framework for this applica-
tion is proposed and the prototype that facilitates the management and sharing of both
personal knowledge and computing tools is developed, with the objective of improving
the overall efficiency and productivity of engineering design processes.1

4.2 Blog+DL Framework for Engineering Knowledge Management
Based on the SECI model, this section identifies the features of a KM system
that promote the conversion and interaction of tacit and explicit knowledge among
individuals. A framework for the use of Weblogs and Digital Libraries as building
blocks for a KM system is presented.

4.2.1 Technology Application Framework
Socialization and Externalization
As depicted in the top left-hand quadrant of Figure 4.1, the first part of the
spiral of knowledge creation is interpersonal. It starts when people that share the same
interest gather to interact, socialize and externalize their tacit knowledge by sharing
experiences. A good KM system should help such people to discover each other and
provide them a convenient means for socialization and externalization.
The statistics about blogs in Section 2.3 indicate that blogs can be used as an
effective KM tool in this regard. Blog publication and the social networking features
help people to discover each other and create online social networks of bloggers on which
issues of common interest are expressed and personal opinions, as well as experiences,
are exchanged. When Blogs is used as an engineering KM tool, an engineer may blog
1
The content of this chapter has been published in Vacharasintopchai et al. (2007b).

22

Figure 4.1: Blog, Digital Library, and Knowledge Conversion Modes
(adapted from Nonaka and Takeuchi 1995)

about a design problem that he is trying to solve, e.g., how to setup a proper design
criterion for a construction project. The blog entry may describe the engineer’s view of
the problem as well as the available approaches, and may also contain initial research
work, such as hyperlinks to other blog entries about “lessons learned” from similar
projects. When published to the Internet or a corporate intranet, the blog entry can
be read by many other people, some of which may share the same interest or have
experienced the same problem. A reader may assist the engineer in finding a solution
by posting his opinion or experience, such as the limitation of a selected approach,
as a comment to the blog entry. Another reader may view the problem differently
and may not agree with the first reader’s opinion. He may express his opinion on a
personal blog and notify the original blogger in the form of a trackback. The original
blogger can participate in the discussion thread created by this series of comments and
trackbacks and be able to find a more appropriate design criterion for his project. This
blog discussion thread may also benefit a public audience who may have had a similar
problem and comes across this discussion thread on a Web search engine. In this way,
a group of people who share the same interest is gathered; a field of interaction is
constituted; and the participants are equipped with a tool to socialize and exchange
experiences, i.e., pieces of tacit knowledge, in blog discussion threads.
Combination and Internalization
The second part of the spiral of knowledge creation is intrapersonal. It contin-
ues from the interpersonal part when a person synthesizes new explicit knowledge—
combining different bodies of explicit knowledge to solve an unfamiliar problem. Once

23

the new knowledge is put into practice, it becomes valuable know-how that is internal-
ized into the person’s tacit knowledge base and becomes part of his skills. An effective
KM system should help people to easily build up repositories of explicit knowledge and
allow relevant pieces of knowledge in the repositories to be conveniently and precisely
retrieved so that a more complete set of knowledge is accessible and can be combined
into the best possible new knowledge. A desirable KM system should also facilitate
internalization, i.e., help people put new knowledge into practice by, for example, pro-
viding them convenient access to relevant computational software tools to shorten the
time required for a design workflow.
Keyword tagging and the selective tag browsing features of Blogs complement
the metadata rich content archiving and retrieval features of Digital Libraries and
make them suitable as effective KM tools in this regard. Blog entries created during
socialization and externalization are typically tagged with keywords when they are
published. In addition to conventional keyword and full text searches, the blog owner
and readers can use the selective tag browsing feature available in most blog service
providers to filter and browse only through relevant blog entries, allowing them to
stay focused on the topics of most interest to them. An engineer can also archive
the content and metadata of mature blog discussion threads, which contain pieces
of externalized tacit knowledge and reflects the group thinking processes by which
solutions to important problems are arrived at, into the collections of the institutional-
repository digital library of which he has a membership. He can also archive interesting
Web pages, such as product data sheets, and digital content, such as Excel, PowerPoint,
PDF, Word, and MP3 files, into the collections. In this way, the engineer builds
up a personal portfolio of knowledge that can be shared across the organization. A
digital library, with its extensive metadata browsing and search features, as well as
full-text indexing capability, becomes a human-filtered search engine (Takeda, personal
communication, February 26, 2007) which portfolio owners and peers can use to access
a focused set of relevant knowledge without “noise.” With all the mentioned features,
Blogs and Digital Libraries can thereby enhance the efficiency and effectiveness of the
combination and internalization processes of individuals.

4.2.2 System Architecture
A system architecture that corresponds to the proposed technology application
framework is presented in Figure 4.2. Seven components, namely, the Web Browser,
the Blog Server, the Digital Library Server, the World Wide Web, the Archiver, the
Keyword Suggester, and the Web Service (WS) Portfolio Manager, are involved. The
latter three are the key components that, together with Weblog and Digital Library
technologies, enable the constitution of an effective knowledge management system.
The Web Browser is proposed as the primary user interface for convenient and
ubiquitous access to the system and also because Blogs and Digital Libraries are them-
selves Web-based. An engineer can use a Web browser to create an account on a blog
server and a digital library, and use it to log on to the blog server to socialize and
externalize his knowledge by publishing blog entries or joining blog discussion threads.
The Archiver is a special-purpose component that monitors requests from a
Web browser to take a “snapshot” of a blog discussion thread or a Web page and
archive it to a specific collection in a digital library. It is the primary component that
assists in building up personal knowledge portfolios. When a blog discussion thread has
matured, the blog owner may summarize the discussion and use a Web browser to take

24

Figure 4.2: Blog+DL Framework for Computer-Aided Engineering Design

25

a snapshot of the thread. The Web browser would prompt him to enter appropriate
metadata, such as the title, the author’s name, keywords, and a brief description of the
thread, as well as his digital library account name and the name of the collection, i.e.,
knowledge portfolio, to which he is authorized to submit. It would then submit the
collected information along with the URL of the thread to the archiver and request that
a snapshot of the thread be taken and processed accordingly. In addition, an engineer
may use the Web browser and the archiver to take snapshots of interesting Web pages,
such as discussion threads, product datasheets, or design tips, into his portfolio. During
the combination and internalization processes, when explicit knowledge from various
sources is crystallized into new explicit knowledge and put into practice, the engineer
then uses the Web browser to access relevant discussion threads in blogs and pieces of
knowledge collected in personal portfolios in the digital library.
The Keyword Suggester is a second essential component in the KM system
proposed here. Since the Blog and Digital Library are independent technologies that
do not naturally interoperate; the use of common metadata is necessary to enable
such interoperation, i.e., the keywords assigned to blog entries and digital library items
should be from the same controlled set and the bibliographic information assigned to
them should conform to the same data format (e.g., DD-MM-YYYY for dates) so that
relevant knowledge in blogs and the digital library can be retrieved simultaneously with
a common search query. The Keyword Suggester is the component that ensures the
enforcement of common metadata.
Besides exchanging and commenting on traditional documents and multimedia
content, an engineer can participate in the SECI modes of knowledge conversion by
exchanging and commenting on computational tools, such as mathematical routines,
numerical analysis modules, and design shortcuts. An engineer can develop these
computational tools as subroutines (or methods, in the object-oriented programming
paradigm) in his preferred programming languages, such as Java or VB.NET, and al-
low them to be remotely executed by other computers as Web services on the Internet.
To “publish” a Web service, i.e., to make a subroutine available for remote access, an
engineer places the code of the subroutine onto a Web service server, such as Apache
Axis for Java or Microsoft Internet Information Server for VB.NET. The server au-
tomatically generates a “Web service description document” that corresponds to the
subroutine. This document contains the instruction for computers and people on how
to execute the service. The engineer can post the Web service description document in
a blog entry, along with a brief description of the service, its theoretical explanation,
and a user’s guide. In this way, the service is advertised and made accessible to the
public. Discussions about the service, such as user experiences, can be exchanged as
blog comments and trackbacks as in the ordinary socialization and externalization use
of blogs. The Web services published through blog entries are discovered primarily
when the service advertisement entries are visited by fellow bloggers. They may also
be discovered secondarily when referred to in other discussion threads. Besides reading
the contents, e.g., descriptions and explanations, blog readers can execute the service
or build up a portfolio of computational tools, i.e., Web services.
This availability of the portfolio of Web services necessitates the third compo-
nent being suggested in the framework, namely, the Web Service Portfolio Manager.
This third key component turns the proposed framework into a fully-functioning KM
system by enabling the sharing and execution of Web services published in blog discus-
sion threads. This add-on component to the blog server detects whether the content

26

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