A Structural Engineering Support System using Semantic Computing


Published on

Issue Date: Dec-2007

Type: Thesis

Publisher: Asian Institute of Technology

Abstract: Practicing structural engineers are often faced with limitations in available computing tools as well as insufficient access to relevant knowledge for design projects and engineering jobs. At the launch of a project the immediate 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 technologies. 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 collaborative structural engineering support system; second, the Semantic Web Services Framework for Computational Mechanics (SWSCM), a methodology 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 several parties. SWSCM can use Blog+DL to deploy and discover shared computing 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 engineering tools that improve the productivity of individual engineers and thereby enhance the competitiveness of engineering firms.

URI: http://dspace.siu.ac.th/handle/1532/121

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A Structural Engineering Support System using Semantic Computing

  1. 1. A STRUCTURAL ENGINEERING SUPPORT SYSTEM USING SEMANTIC COMPUTING by Thiti VacharasintopchaiA 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
  2. 2. ACKNOWLEDGMENTS I would like to thank the Royal Thai Government for granting me adoctoral research fellowship at the Asian Institute of Technology. My pro-found gratitude is due to Professor Vilas Wuwongse and Professor WorsakKanok-Nukulchai, my mentors and advisors who invariably gave highlyvaluable guidance, inspirational suggestions, encouragement and supportthroughout this research, to Professor Kincho H. Law from Stanford Uni-versity who gave highly valuable suggestions as the external examiner, andto Dr. William Barry, former doctoral advisor, master’s thesis advisor andgood friend, who introduced me to the world of scientific computing. Iwould also like to express sincere appreciation for Dr. Pennung Warnitchaiand Dr. B. H. W. Hadikusumo, doctoral committee members, who gaveinvaluable guidance and suggestions. I am grateful to Dr. Pannapa Her-abat for her unfailingly support and to Dr. Wisit U. Pongsa, my formeremployer, who gave several opportunities for conferences and professionaltraining which form the basis of the work in this dissertation. Dr. Vo-ratas Kachitvichyanukul provided useful comments during the early stagesof this research. Wouter Rosingh was of great help particularly towardsthe end. I wish to thank my parents, Chavalert Vacharasintopchai andBenjawan Chatnarat, my younger brothers, Nithi and Nipith, family mem-bers, Apsara Narat and Gallissara Agavatpanitch for their love, supportand encouragement. Thanks are due to friends, especially Dr. DussadeeSatirasetthavee, 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 theKnowledge Representation Laboratory and the School of Engineering andTechnology, especially Yupa Soodjitporn and Worranuch Chumchat, areappreciated. I am also grateful to the Asian Development Bank and theGreater Mekong Subregion Academic Network for funding the knowledgemanagement part of this research. ii
  3. 3. ABSTRACT Practicing structural engineers are often faced with limitations in avail-able computing tools as well as insufficient access to relevant knowledge fordesign projects and engineering jobs. At the launch of a project the im-mediate tasks invariably involve research, discussions with colleagues anda series of computations. These tasks are repeated over and over againuntil a final design is arrived at. The degree to which these tasks are wellfacilitated in an engineering firm helps determine the productivity of itsengineers and thus the firm’s competitiveness in the industry. Semanticcomputing technologies such as Semantic Web Services, Weblog, and Dig-ital Library have substantial, hitherto untapped potential to improve theproductivity of structural engineers. This dissertation proposes a supportsystem for structural engineering using these semantic computing technolo-gies. A system architecture and the key software components for it arespecified and prototype systems presented. The support system involvestwo frameworks of software infrastructure: first, the Weblog and DigitalLibrary 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 WebServices Framework for Computational Mechanics (SWSCM), a method-ology that unifies and utilizes scattered computing resources. Blog+DLand 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, includingpeer opinions about underlying theories and user experiences. Two proof ofconcept prototype systems were developed. The first system illustrates thejoint application of Blog+DL and SWSCM to build support systems thatfacilitate the computationally oriented knowledge management process instructural engineering. The second system illustrates the full potential ofSWSCM to facilitate a computationally intensive workflow that involvesheterogeneous engineering software components. Blog+DL and SWSCMcombine semantic computing technologies to build computer-aided engi-neering tools that improve the productivity of individual engineers andthereby 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
  4. 4. TABLE OF CONTENTSChapter 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
  5. 5. 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 315 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 566 Conclusions 59 6.1 Conclusion and Discussion 59 6.2 Recommendations for Further Research 61 References 63 v
  6. 6. LIST OF FIGURESFigure 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
  7. 7. LIST OF TABLESTable Title Page 6.1 Features Comparison with Existing Work 60 vii
  8. 8. CHAPTER 1 INTRODUCTION1.1 Motivation Two obstacles that practicing structural engineers often face in a design projectare limitations in available computing tools and limited access to relevant knowledge.Several tasks are involved when a project is launched, in terms of research, discussionwith colleagues and computational workflow. These tasks are repeated over and overagain 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, andthe software itself often does not fully address their needs. Some firms may share acommercial software package or use it on a pay-per-use basis; whereas others developsoftware or subroutines in-house. Some in-house tools may be so well-developed orhave such well-developed components that it is more productive for engineers to reusethem rather than to develop new systems from scratch. However, both commercialand in-house tools are typically based on specific assumptions that need to be satisfiedbeforehand, and their input and output data are usually based on specific conventionsand formats that must be strictly followed to obtain sensible results. As the numberof shared tools grows, it becomes difficult for users to find appropriate tools that fittheir design problems best, and it is tedious for them to manually adapt the potentiallyincompatible input and output data of the tools to a computational workflow, such asthe 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. Forless common projects, significant time is typically invested in research to come up witha solution strategy with the assistance of senior engineers or experts. This processis time consuming and can become a bottleneck if not well facilitated. Many knowl-edge management solutions are available in the literature or as commercial softwarepackages. However, most of them aim single-sidedly to capture tacit knowledge fromexperts into a knowledge repository, rather than facilitating the sharing, discussingand developing of new knowledge among individuals, which are key practices for thesustainable improvement of knowledge in an organization according to the theory oforganizational knowledge creation (Nonaka and Takeuchi, 1995).1.2 Problem Statement This research aims to create a support system that improves the productivity ofstructural engineers during the engineering process and, particularly, to alleviate thetwo 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
  9. 9. 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 arerequired to ensure that the obstacles specific to structural engineering are addressedand the latter are required to ensure the scalability and sustainability of the proposedsolution.1.3 Objectives The primary objective of this study is to develop a support system that improvesthe productivity of structural engineers during the engineering process. To fulfill thisprimary 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 computationalinteroperability in the structural engineering context. Beyond the literature review 2
  10. 10. traditional for research of this type, the work is anchored in observations of real worldconstraints on practicing engineers in engineering consulting firms as experienced bythe 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 sharingand interoperability of computational tools. The former facilitates the creating andsharing of knowledge in the form of multimedia files and computational services. Itallows users to exchange opinions about posted issues and services and to save someof them to personal knowledge portfolios for later reference. It also allows access toshared computational tools which can be invoked and interoperated by the softwareinfrastructure developed in this study. To demonstrate the full potential of this infras-tructure, a prototype is separately developed to illustrate an intelligent collaborationof many structural analysis software agents located on personal computers and parallelcomputer clusters on the Internet to solve problems in linear elasticity. The implementation of the system prototypes is based primarily on open-sourcesoftware 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 theprototypes. As the term “semantic” implies, artificial intelligence techniques are usedextensively.1.5 Contributions This study is aimed at improving the productivity of structural engineers inthe engineering process by dealing with computational workflow and knowledge man-agement issues using semantic computing. The results of this study are an intelligentstructural 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 toolsindividually developed and located on heterogeneous computer platforms intelligentlycollaborate. It is expected that the application of the frameworks and support systemdeveloped and demonstrated in this study can alleviate computational and knowledgemanagement problems in project design and other engineering work and thus improvethe 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 backgroundinformation about the theory, technologies and research that pertain to the develop-ment of support systems for the engineering process. Chapter 3 describes commonobstacles in the structural engineering process, identifies necessary features and con-ceptualizes the general design for structural engineering support systems. Chapter 4presents the structural engineering support system using semantic computing and de-scribes the architectural framework on which it is based. Chapter 5 addresses theinteroperability of computing resources which enables the sharing of computationaltools via support systems. It proposes a framework for the interoperation of structuralengineering software modules and demonstrates the framework by a system prototypeand a case study. Chapter 6 concludes this dissertation and discusses the benefits andlimitations of the presented work, with recommendations for further research. 3
  11. 11. 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 thedata 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 structuralengineering processes using semantic computing is presented in this chapter. First, thetheoretical background of knowledge management (KM) and two illustrative KM sys-tems for engineering design firms are presented. Weblogs, a socialization and knowledgesharing enabling technology for the Web, and Digital Libraries, a technology for fastand accurate archiving and retrieval of electronic documents and multimedia contentin organizations, are discussed next. Web Services technology, a modern paradigm fordistributed computing on the Internet is then presented. Finally, the Semantic Web, anemerging technology that brings artificial intelligence to the Web, and Semantic WebServices, the joint application of Web Services and the Semantic Web, are discussed.2.1 Knowledge Management2.1.1 Definition and Significance Knowledge management is a process for optimizing the effective application ofintellectual assets to achieve organizational objectives (Pollock, 2001). It is “the disci-pline of creating a thriving work and learning environment that fosters the continuouscreation, aggregation, use and re-use of both organizational and personal knowledge inthe 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 andaction (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 aparticular stance, perspective, or intention” and involves an action “to some end;”whereas information and data are the “necessary medium or materials for eliciting andconstructing knowledge.” Knowledge, data, and information however are alike in thatthey are “context specific” and “relational” (Nonaka and Takeuchi, 1995, p. 58). Indecision making terms, knowledge contains the least “noise” whereas data contains themost noise pertaining to a decision making process (Sheehan et al., 2005). Businessorganizations that manage the creation and the application of knowledge more effec-tively will be more competitive than others. Organizations that better manage andshare knowledge from past experiences of their peers will more effectively utilize theirresources 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 fashionto minimize cost and stay competitive. This competitive environment has made KMattractive in engineering consultant firms (Carrillo and Chinowsky, 2006; Mezher et al.,2005). KM helps an engineering consulting firm to leverage their technical skills andstore them in a manner to speed up the work, and compete with others in such a waythat projects are produced with high quality in less time (Mezher et al., 2005). As theworld enters the “knowledge society,” the basic economic resource is no longer capital, 4
  12. 12. 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 tacitknowledge. Explicit knowledge can be articulated in formal language such as grammat-ical statements, mathematical expressions, and manuals, which can be processed by acomputer with relative ease. Tacit knowledge on the other hand consists of personalknowledge embedded in individual experience and involves intangible factors such aspersonal beliefs, perspectives, and value systems, which are difficult to articulate com-pletely in formal language. Because of its subjective and intuitive nature, it is difficultto 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 oftacit knowledge, and often tacit knowledge is regarded as the more important kind ofknowledge (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” (Brownand Duguid, 2000, as cited in Quintas 2005) and “A master craftsman ... develops awealth of expertise at his fingertips after years of experience. But he is often unableto articulate the scientific or technical principles behind what he knows” (Nonaka andTakeuchi, 1995, p. 8).2.1.3 Theory of Organizational Knowledge Creation Knowledge is dynamic in nature. Nonaka and Takeuchi (1995) proposed thetheory of organizational knowledge creation which postulates four temporal modes ofconversion among tacit and explicit knowledge, namely, (1) socialization from tacitknowledge to tacit knowledge, (2) externalization from tacit knowledge to explicitknowledge, (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 tacitknowledge” such as shared mental models and technical skills, helps an individualto acquire tacit knowledge directly from others through observation, imitation, andpractice in a field of interaction. Externalization, the process of “articulating tacitknowledge 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 computerizedcommunication channels so that they are “crystallized” into a new piece of knowledge,product, service, or methodology. Formal education and training in schools usuallytakes the combination form of knowledge conversion. Internalization, the process of“embodying explicit knowledge into tacit knowledge,” is the process through whichexperiences of individuals, obtained through socialization, externalization, and combi-nation, are internalized into their tacit knowledge bases in the form of shared mentalmodels or technical know-how. Internalization is closely related to “learning by do- 5
  13. 13. ing.” Documentation helps individuals to internalize what they experienced and alsofacilitates the transfer of explicit knowledge to other people. Each mode of knowledge conversion naturally produces different knowledgetypes: 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 spiralfashion: sympathized knowledge may become explicit conceptual knowledge throughsocialization and externalization. The conceptual knowledge produced then becomes aguideline for creating systemic knowledge through combination. The systemic knowl-edge is next converted to operational knowledge through internalization. When anindividual socializes with colleagues and externalizes his knowledge, his operationalknowledge consequently triggers a new cycle of knowledge creation. Knowledge conver-sion is a social process between individuals and is not confined within an individual. Ashuman 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 explicitknowledge are constantly “amplified” through shifts between these social conversionprocesses. The four temporal modes of knowledge conversion among individual staffmembers, 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 theSocialization-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 isinconceivable without knowledge. Even though the phrase “knowledge management”came into common usage in the mid-1990s, the management of knowledge processesbegan long before the term was coined, without the KM label (Quintas, 2005). KMis different in each organization. There is no one right way and organizations developdifferent 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 lessconcerned with addressing real knowledge issues than informal or existing processesthat are not so labeled” (Quintas, 2005). The focus of these initiatives and toolswas often on capturing explicit knowledge, rather than facilitating the conversion andsharing of tacit and explicit knowledge, as in the SECI model. Their scope can thusmore 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 firmsrather than for construction managers or contractors. The nature of their work differsfrom 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 proprietarytechnologies that do not conform to Internet standards other than basic HTTP and 6
  14. 14. HTML for Web publication. Cross-system interoperability and advanced applicationsof Web technologies, such as the Semantic Web (Berners-Lee et al., 2001) and Web feeds(Wikipedia, 2007e) cannot therefore be implemented without major modifications ofthese 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 managementsystem, 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. Thesystem 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 designprocedures prepared by engineers, lessons learned from construction sites and knowndesign hints and shortcuts, (2) the capturing of explicit and tacit knowledge createdby the first process, (3) the refining of knowledge through the review and approvalby group leaders and directors, (4) the storage of useful knowledge in a repositoryfor 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 aformat useful to users. The system consists of shared folders on the department’s fileserver repository at the head office and a Web-based user interface that helps usersto 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 ofcompleted project drawings and documents, pending project drawings and documents,department-specific software and worksheets, libraries of AutoCAD shortcut menusand programs as well as document templates and forms, all of which go through thesix 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 andprocedures, 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 retrievalprocess is assisted by a Web interface, it is not clear whether the knowledge creationand review workflow and repositorial publication, which are part of the externalizationprocess, are computerized or automated. Metadata, as well as keyword and full-textsearch features, which are information and tools necessary for precise retrieval of knowl-edge from large knowledge bases or nested folder hierarchies, are not mentioned. Thesystem 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 toenable peer discussion and collaboration, leaving the socialization aspect of the SECImodel relatively unfacilitated.2.2.2 ADD and Arup Intranet Systems Jonathan Cohen & Associates (2004) developed intranet KM systems for ADDInc. and Ove Arup and Partners. ADD is a medium-sized multidisciplinary design firmwith 150 employees and three offices in Cambridge, Massachusetts, San Francisco andMiami; whereas Arup is a very large engineering consultant firm with 5,000 employeesin 60 offices and 40 countries. 7
  15. 15. The ADD intranet is a unified Web portal to internal firm information. ADDstaff are directed to a “today” homepage when they enter the site where they arepresented with updated events, news and announcements stored on a shared calendardatabase. From the homepage, they can navigate to information about firm standardprocedures, document templates and CAD details. Meetings can be scheduled usingthe calendar function; and pictures and news items can be shared among colleaguesthrough personal pages assigned to each user. These personal pages help to create asocial network across the three offices. A list of experts within the firm is also postedon 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 withinthe 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 adatabase that tracks how specific details have been implemented in projects. Arup’sengineers can annotate their experience with the details and specifications, such as howthey worked in one project and failed in another, and so capture the feedback loopbetween design and implementation. These experience records and the feedback loopare invaluable information and practice that help distinguish Arup in the marketplace.Employees of Arup are also allowed to “volunteer” information through personal Webpages where they can quickly and easily share their interests and expertise with therest of the firm (Sheehan et al., 2005). In this way individuals can socialize and jointhe SECI knowledge spiral which drives innovation at the firm. The ADD and Arup intranet systems are examples of systems that conform toKM best practice but rely on proprietary techniques. Cross-system interoperabilityand advanced applications of recent Web technologies, which can further improve theproductivity of engineers, cannot thus be implemented easily.2.3 Weblogs and Digital Libraries2.3.1 Weblogs A weblog, or a blog, is a Web-based journal publication which consists primarilyof periodic articles, normally in reverse chronological order (Wikipedia, 2007b). Eacharticle in a blog is called a blog entry. A person who publishes to a blog is called ablogger . 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) thecomment section where readers and the blogger can discuss and exchange experiencesand opinions about issues raised in the blog entry, and (6) the trackback (Trott andTrott, 2002) hyperlink where the readers who blog about the blog entry can notifythe original blogger about the existence and the content of their blogs. Hyperlinks,comments, trackbacks, and citations in blogs create online social networks and promotethe 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, torandom thoughts. A blog entry usually contains some hyperlinks to other Web docu- 8
  16. 16. ments which are the subjects of discussion. To publish a blog entry, a blogger logs onto 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 titleand 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 apublish button is clicked, the blog entry is instantly published to the Internet and canbe accessed by friends, colleagues, and the public through regular Web browsers, Websearch 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-mailsto a secret e-mail address to publish the contents and picture attachments directlyfrom an e-mail client software on a laptop computer or a mobile phone. Blogging thusallows 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 millionsblogs on the Internet, with 120,000 new blogs added each day, and 1.5 million posts perday. 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 blogsare 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 comprisesfocused collections of digital objects, including text, video, and audio, along with meth-ods for access and retrieval, and for selection, organization, and maintenance of thecollections (Witten and Bainbridge, 2003), all of which support life-long learning, re-search, scholarly communication and preservation (Wikipedia, 2007d). Digital librarieswere originally used to archive the digitized copies of rare documents, books, and thepictures 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 technologicalobsolescence (DSpace, 2007b). A digital library system is often developed such that it is accessible on theWeb, 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 appropriatemethods of access and retrieval, can be regarded as digital libraries, unless metadata(data about data), which are important information to precisely catalog, locate andretrieve 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 ofobjects based on them, are facilitated by the system (Witten and Bainbridge, 2003).The metadata typically used in digital libraries include bibliographic information andsubject keywords, much as the indexing information used in physical libraries. The users of a digital library system have one of two roles, namely, the publisherand 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 tocapture (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 thegeneral users take the reader role to browse, search, and consume information in the 9
  17. 17. 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 Modelcollections. In other digital library implementations, such as DSpace (MIT Librariesand Hewlett-Packard Company, 2007) which is based on an institutional repositoryconcept, select users may also publish to some collections in the repository, thus takingboth the publisher and the reader roles. Computers and users can locate objects acrossheterogeneous digital library systems by using standard query protocols, such as theOpen 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 aknowledge base which stores explicit and externalized knowledge from the users. Themetadata-rich search feature of the system also allows users to precisely and promptlylocate pieces of knowledge which can be used to solve their specific problems in a timelyfashion. In this sense, a digital library system can thus promote the combination andinternalization of knowledge in the SECI model.2.4 Web Services Web Services (W3C, 2004d) is a modern paradigm of distributed computingthat uses the Internet as the communication medium. Its fundamental concept is tobuild computer software by making use of remote procedure calls (RPC) over the In-ternet or an intranet. It differs from other distributed computing technologies, forexample CORBA (OMG, 2005) and DCOM (Microsoft, 1996), in the use of platform-independent standards, based on HTTP (Fielding et al., 1999) and Extensible MarkupLanguage (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 theservice 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 someresearchers, which simply refers to the “ability to access many services through theInternet” (Chen et al., 2006) via an arbitrary communication protocol without regardto the specific World Wide Web Consortium (W3C) standards that are introduced inthis 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
  18. 18. certain functionality in their organization as a Web service that is made available forany other organization to invoke. To be effective, a Web service needs to performtwo tasks. First, it needs to describe the Web service in a standard format thatis understandable by all organizations that will be using that Web service. Next itneeds to publish the details about its Web service in a central registry that is publiclyaccessible. A service consumer is any organization that uses Web services provided bya service provider. It knows the functionality of a particular Web service from thedescription made available by the service provider. To retrieve those details, a serviceconsumer searches the registry where the service provider has published its Web servicedescription. The service consumer can then obtain the description of the requiredmechanism, 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 Webservices, and where service consumers search for them. Service providers usually pub-lish their Web service capabilities in the service registry for service consumers to findand later, if interested in the provided service, bind to them. Examples of informationtypically 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 serviceas well as the input and output formats for its invocation. Web services take multiple roles on different occasions. A Web service takesthe “provider” role when it accepts requests from consumers to be processed. It takesthe “consumer” role when it delegates its tasks to other Web services. A Web serviceprovider takes the special role of “service registry” if it helps a service consumer todiscover a service provider for later binding. A client program that interacts onlywith end users takes the consumer role when it requests a service from a Web serviceprovider. The Web Services architecture aims to achieve interapplication communication,irrespective of the programming language that the application is written in or theplatform that the application is running on, by three fundamental operations whichare “finding,” “binding,” and “publishing.” To make this happen, standards for eachof these three operations and a standard way for a service provider to describe theirWeb services are needed. The Web Service Description Language (WSDL) (W3C, 2001) is such a standardthat uses the XML data format to describe Web services. The WSDL document for aWeb service defines the methods that are present in the Web service, the input/outputparameters for each method, the data types, the network transport protocol used, andthe URLs where requests for services are to be submitted and from which the resultscan 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 astandard for service consumers to find service providers and details about their Webservices. Publication of the details is the “description” part of UDDI and the locationof such details is the “discovery” part of it. Publication of these details is typicallyachieved 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
  19. 19. lightweight XML communication mechanism to exchange information between appli-cations, regardless of the operating systems, programming languages, or object modelsemployed in their development. SOAP may be visualized as a “carrier” of data encodedin XML format. Web Services technology has been involved in many areas of scientific comput-ing, ranging from computational infrastructure developments (Chiu et al., 2002; vanEngelen, 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 thestandardization attempt by the Global Grid Forum (GGF, 2003) to create the OpenGrid Service Architecture specification (OGSA) (Foster and Gannon, 2003) which isthe 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 thereare 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 theTeraGrid (Catlett, 2002), imposition of such a standard is likely to increase the numberof scientific computing applications that rely on Web Services technology significantly.2.5 The Semantic Web2.5.1 Background The Semantic Web (Berners-Lee et al., 2001) is a vision for the next generationof the Web on which information will be useful and meaningful not only for peoplebut also for computers. Computers will be able to understand pieces of informationon Web pages rather than merely presenting them to users, and will thus be able toautonomously assist users in manipulating the information. The Semantic Web is not a separate Web. Rather, it is an evolution of thepresent Web into one in which information is given well-defined meaning (Berners-Leeet al., 2001). Documents on the present Web can be transformed into Semantic Webdocuments by augmenting them with metadata aimed at computers. Metadata is dataabout other data that enables computers to determine the meaning of informationin a Web document by following hyperlinks to definitions of key terms and rules forreasoning about them logically. The Semantic Web relies on two existing technologies which are XML and theResource 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 thatannotate Web pages or sections of text on a page. Computer programs can make use ofthese tags in many ways, from rendering XML documents in a human-friendly formaton Web browsers, to mapping XML documents as data sources. However, XML tagsare 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 inorder to develop programs that properly manipulate the XML tags. The intendedmeaning of an XML tag will be understood if the name of the tag is drawn from acommon dictionary. RDF (W3C, 1999b) is a language for describing information about resourceson the Web. Typically encoded in XML format itself, the RDF language allows Web 12
  20. 20. 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 ofan 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. Aset 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 constitutesan ontology of the domain. In the context of the Semantic Web, an ontology is adocument 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 RDFproperty 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 linkWeb pages are the most common type of URI. URIs ensure that terms or concepts arenot just words in a document but are tied to a unique definition that anyone can findon the Web (Berners-Lee et al., 2001). In addition, the object of an RDF statementcan be another RDF statement, so that complex RDF statements can have other RDFstatements nested inside in multiple layers. Figure 2.2 illustrates the RDF statementsserving 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 theURI http://stweb.ait.ac.th/~ st029284/foaf.rdf#thitiv. Ontologies and RDF statements are typically stored on Web servers for laterretrieval 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 anRDF statement that employs terms from specific ontologies inside hidden tags of Webdocuments themselves (Palmer, 2002). Software tools such as the Jena Semantic WebFramework (Jena, 2007) provide a means to retrieve ontologies and RDF statementsfrom Web sources and store them in a local knowledge base. The set of inferencerules that correspond to the semantics of the RDF constructs (W3C, 2004c) is prebuiltinto Jena. Answers to queries such as “give me a list of Web pages authored byhttp://stweb.ait.ac.th/~ st029284/foaf.rdf#thitiv” can be obtained throughJena’s Application Programming Interfaces (APIs) (Wikipedia, 2007a).2.5.2 How the Semantic Web Works The following examples illustrate how Web documents, XML, ontologies, andRDF statements work on the Semantic Web.Example 1 John Doe is at the Reinforced Concrete Design Worksheet Web page on the CEResources Website (Vacharasintopchai, 2005). He is interested in the link mentioned onthe page and wants to contact the author for more information about it. The author’se-mail address or contact information is not given on the Web page. How can Johnfind the author’s e-mail address?Traditional Solution. On the Web page, John has to find the name of the author. 13
  21. 21. <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> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"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 <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" 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
  22. 22. 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” fromthe 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, collectivelyrefers to the statements that are represented by the RDF language (W3C, 2003) as wellas the Web Ontology Language (OWL) (W3C, 2004a), which is an extension of the RDFlanguage for more comprehensive representation of knowledge on the Semantic Web.OWL provides several additional constructs, such as “equivalent class,” “intersectionof,” “union of,” “inverse of,” and “disjoint with,” that more precisely capture bodiesof knowledge over those available in the RDF language. Henceforth, the term “OWLstatement” may be used when it is desirable to emphasize the employment of OWL’sadditional constructs in the statement.2.6 Semantic Web Services2.6.1 Background Web Services introduced a new model of distributed computing that uses theInternet as the communication platform. The present technology based on WSDL,SOAP, and UDDI allows automatic publication, discovery, and execution of services atthe 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 semanticproblem of XML discussed earlier. To make proper use of the Web services advertisedby WSDL documents, programmers have to know in advance the intended meaning 15
  23. 23. of the custom tags that specify the input and output schemas as well as the names ofservices that a Web service provides. Like other XML documents, WSDL documentsare 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 Webservice that calculates the sine of a “degree” angle to autonomously execute and getthe 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 servicesthat calculate the sine values of angles. Semantic Web Services, the idea of augmenting Web service descriptions usingSemantic Web technology (Burstein et al., 2005), were introduced to address this kindof problem and to facilitate the autonomous publication, discovery, and execution ofservices at the semantic level. Semantic Web service description languages, such asOWL-S (OWL–S, 2003) and Web Service Modeling Ontology (WSMO) (Roman et al.,2005), were proposed as abstractions of syntactic Web service description languagessuch as WSDL, which are used in current Web Services technology. They are meantto be used with semantic matchmakers, which are the software agents that accept andkeep track of the descriptions of available services from providers and match themagainst the requirements from service consumers (Burstein et al., 2005). Matchmakersenhance 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 groundingconstructs for specialization into WSDL constructs for compatibility with existing Webservices, which are described by WSDL documents. OWL-S is used as the semanticWeb 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 theGetSineValue Web service to get the correct value of sine from the TrigonometricSineWeb 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 aservice 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
  24. 24. <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
  25. 25. CHAPTER 3 DEVELOPMENT OF A SUPPORT SYSTEM FOR STRUCTURAL ENGINEERING3.1 Structural Engineers and Construction Projects Construction projects start when owners decide to have buildings constructed tosatisfy their needs. Through bidding processes or fast track approaches, an architect iscontracted, the owner’s requirements collected and a preliminary design agreed upon.A series of drawings and specifications which consist primarily of architectural andstructural work are indispensable for the materialization of a project. Tender drawingsand specifications are required for bidding to select a general contractor for the project.Detailed drawings from designers and shop drawings from field engineers are necessaryfor 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 structuralsystems that are appropriate for preliminary designs. Structural engineers are con-sulted for the estimated size of columns, beams and slabs before architectural drawingsare developed for tender. Detailed foundation designs are often the first constructiondrawings requested from structural engineers, even though the information for themis last obtained in the analysis and design process. In addition, field engineers needprompt 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 aconstruction project; yet structural engineers are often on the critical path with tightschedules and are allocated very limited time for their tasks. Two obstacles that hinderthe productivity of structural engineers in dealing with their tasks are limitations inavailable computing tools and limited access to relevant knowledge. A support systemthat improves the productivity of structural engineers during the engineering processis therefore highly advantageous, as postulated in the first chapter.3.2 Design Principles Structural engineering departments typically have both senior engineers withextensive practical experience and junior engineers (or fresh graduates) with little pro-fessional experience. Many kinds of tools are used in the engineering process. Juniorengineers are often more acquainted with relatively new technologies, whereas seniorengineers have a wealth of practical insight and often know useful shortcuts. Somecommercial software packages are licensed and many departments have special-purposesoftware tools that have been individually developed in-house. Commercial softwareand in-house tools are usually shared among engineers in the department. The outputfrom structural engineers is commonly in the form of hand sketches or CAD drawings,supported by calculation sheets. On completion of projects, relevant documents arecollected 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
  26. 26. 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
  27. 27. 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 variousfunctionalities which can be combined to meet the five requirements for good struc-tural engineering support systems. This section proposes a general system architecturewhich utilizes semantic computing technologies to build such a system. The systemarchitecture consists of five components as depicted in Figure 3.1. Each component islabeled with numbers (1), (2), . . . , (5), which correspond to item numbers of the designprinciples. For example, (1) refers to References and Premises and (2) refers to Knowl-edge and Tools Sharing. The relationships between these components and the designprinciples 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
  28. 28. 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 engineersin dealing with their tasks more productively. Semantic computing technologies wereidentified as enabling tools for such systems and a general architecture for the develop-ment of structural engineering support systems has been proposed. The methodologyto 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 theirannotation in data and knowledge are central to all aspects of the development, hencethe term “semantic computing.” Their importance is illustrated throughout. 21
  29. 29. CHAPTER 4 A STRUCTURAL ENGINEERING SUPPORT SYSTEM USING SEMANTIC COMPUTING4.1 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 ofknowledge management (KM) systems, which can enhance the access to knowledgeand expertise of engineers, as previously postulated. Such systems assist them insharing tacit knowledge, developing concepts from many pieces of tacit knowledge,combining various elements of explicit knowledge, and storing new knowledge in theknowledge bases of individuals and the organization. In such a KM system, an engineeris 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 anddiscussions among colleagues are electronically facilitated and individual blog entriesand discussions can be cross-referenced. After a work-in-progress is completed, relatedblog entries and discussions, as well as a project summary document, can be archivedto a digital library for future reference. This chapter builds on the general design for structural engineering supportsystems described in Chapter 3 to propose a methodology by which blogs and digitallibraries can be combined into a structural engineering support system based on threeenabling 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 bothpersonal knowledge and computing tools is developed, with the objective of improvingthe overall efficiency and productivity of engineering design processes.14.2 Blog+DL Framework for Engineering Knowledge Management Based on the SECI model, this section identifies the features of a KM systemthat promote the conversion and interaction of tacit and explicit knowledge amongindividuals. A framework for the use of Weblogs and Digital Libraries as buildingblocks for a KM system is presented.4.2.1 Technology Application FrameworkSocialization and Externalization As depicted in the top left-hand quadrant of Figure 4.1, the first part of thespiral of knowledge creation is interpersonal. It starts when people that share the sameinterest gather to interact, socialize and externalize their tacit knowledge by sharingexperiences. A good KM system should help such people to discover each other andprovide them a convenient means for socialization and externalization. The statistics about blogs in Section 2.3 indicate that blogs can be used as aneffective KM tool in this regard. Blog publication and the social networking featureshelp people to discover each other and create online social networks of bloggers on whichissues 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
  30. 30. 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 designcriterion for a construction project. The blog entry may describe the engineer’s view ofthe problem as well as the available approaches, and may also contain initial researchwork, such as hyperlinks to other blog entries about “lessons learned” from similarprojects. When published to the Internet or a corporate intranet, the blog entry canbe read by many other people, some of which may share the same interest or haveexperienced the same problem. A reader may assist the engineer in finding a solutionby 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 differentlyand may not agree with the first reader’s opinion. He may express his opinion on apersonal blog and notify the original blogger in the form of a trackback. The originalblogger can participate in the discussion thread created by this series of comments andtrackbacks and be able to find a more appropriate design criterion for his project. Thisblog discussion thread may also benefit a public audience who may have had a similarproblem 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 isconstituted; and the participants are equipped with a tool to socialize and exchangeexperiences, 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
  31. 31. 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 effectiveKM system should help people to easily build up repositories of explicit knowledge andallow relevant pieces of knowledge in the repositories to be conveniently and preciselyretrieved so that a more complete set of knowledge is accessible and can be combinedinto the best possible new knowledge. A desirable KM system should also facilitateinternalization, i.e., help people put new knowledge into practice by, for example, pro-viding them convenient access to relevant computational software tools to shorten thetime required for a design workflow. Keyword tagging and the selective tag browsing features of Blogs complementthe metadata rich content archiving and retrieval features of Digital Libraries andmake them suitable as effective KM tools in this regard. Blog entries created duringsocialization and externalization are typically tagged with keywords when they arepublished. In addition to conventional keyword and full text searches, the blog ownerand readers can use the selective tag browsing feature available in most blog serviceproviders to filter and browse only through relevant blog entries, allowing them tostay focused on the topics of most interest to them. An engineer can also archivethe content and metadata of mature blog discussion threads, which contain piecesof externalized tacit knowledge and reflects the group thinking processes by whichsolutions 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 interestingWeb 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 buildsup a personal portfolio of knowledge that can be shared across the organization. Adigital library, with its extensive metadata browsing and search features, as well asfull-text indexing capability, becomes a human-filtered search engine (Takeda, personalcommunication, February 26, 2007) which portfolio owners and peers can use to accessa focused set of relevant knowledge without “noise.” With all the mentioned features,Blogs and Digital Libraries can thereby enhance the efficiency and effectiveness of thecombination and internalization processes of individuals.4.2.2 System Architecture A system architecture that corresponds to the proposed technology applicationframework 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, theKeyword Suggester, and the Web Service (WS) Portfolio Manager, are involved. Thelatter three are the key components that, together with Weblog and Digital Librarytechnologies, enable the constitution of an effective knowledge management system. The Web Browser is proposed as the primary user interface for convenient andubiquitous 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 blogserver and a digital library, and use it to log on to the blog server to socialize andexternalize his knowledge by publishing blog entries or joining blog discussion threads. The Archiver is a special-purpose component that monitors requests from aWeb browser to take a “snapshot” of a blog discussion thread or a Web page andarchive it to a specific collection in a digital library. It is the primary component thatassists in building up personal knowledge portfolios. When a blog discussion thread hasmatured, the blog owner may summarize the discussion and use a Web browser to take 24
  32. 32. Figure 4.2: Blog+DL Framework for Computer-Aided Engineering Design 25