IGI Global Emerging Technologies For Semantic Work Environments Techniques Methods And Applications Jun 2008 ISBN 1599048779 pdf

Emerging Technologies

for Semantic Work Environments: Techniques, Methods, and Applications Jörg Rech Fraunhofer Institute for Experimental Software Engineering, Germany Björn Decker empolis GmbH–Part of Arvato: A Bertelsmann Company, Germany Eric Ras Fraunhofer Institute for Experimental Software Engineering, Germany

I N FORM AT I ON SCI EN CE REFEREN CE Hershey • New York Acquisitions Editor: Kristin Klinger Development Editor: Kristin Roth Senior Managing Editor: Jennifer Neidig Managing Editor: Jamie Snavely Assistant Managing Editor: Carole Coulson Copy Editor: Jeannie Porter Typesetter: Michael Brehm Cover Design: Lisa Tosheff Printed at: Yurchak Printing Inc. Published in the United States of America by Information Science Reference (an imprint of IGI Global)

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Library of Congress Cataloging-in-Publication Data Emerging technologies for semantic work environments : techniques, methods, and applications / Jorg Rech, Bjorn Decker and Eric Ras, editors. p. cm.

Summary: "This book describes an overview of the emerging field of Semantic Work Environments by combining various research studies

and underlining the similarities between different processes, issues and approaches in order to provide the reader with techniques, methods,

and applications of the study"--Provided by publisher.

ISBN-13: 978-1-59904-877-2 (hbk.)

ISBN-13: 978-1-59904-878-9 (e-book)

1. Semantic Web. 2. Semantic networks (Information theory) 3. Information technology--Management. I. Rech, Jorg. II. Decker, Bjorn.

TK5105.88815.E44 2008 658.4'038--dc22 2007042680 British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library.

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Table of Contents

Foreword ............................................................................................................................................ xiv

Preface ................................................................................................................................................ xvi

Acknowledgment ..............................................................................................................................xxiii

Section I

Introduction

Chapter I Enabling Social Semantic Collaboration: Bridging the Gap Between Web 2.0 and the Semantic Web ................................................................................................ 1 Sören Auer, University of Pennsylvania, USA Zachary G. Ives, University of Pennsylvania, USA Chapter II Communication Systems for Semantic Work Environments ................................................................ 16 Thomas Franz, University of Koblenz-Landau, Germany Sergej Sizov, University of Koblenz-Landau, Germany Chapter III Semantic Social Software: Semantically Enabled Social Software

or Socially Enabled Semantic Web? ..................................................................................................... 33

Sebastian Schaffert, Salzburg Research Forschungsgesellschaft, Austria

Section II

Semantic Work Environment Tools

Chapter IV SWiM: A Semantic Wiki for Mathematical Knowledge Management ................................................. 47 Christoph Lange, Jacobs University Bremen, Germany Michael Kohlhase, Jacobs University Bremen, Germany

Chapter V CoolWikNews: More than Meet the Eye in the 21st Century Journalism ............................................................................................................................................. 69 Damaris Fuentes-Lorenzo, University Carlos III of Madrid, Spain Juan Miguel Gómez, University Carlos III of Madrid, Spain Ángel García Crespo, University Carlos III of Madrid, Spain

Chapter VI Improved Experience Transfer by Semantic Work Support ................................................................. 84 Roar Fjellheim, Computas AS, Norway David Norheim, Computas AS, Norway Chapter VII A Semi-Automatic Semantic Annotation and Authoring Tool

for a Library Help Desk Service ......................................................................................................... 100

Antti Vehviläinen, Helsinki University of Technology (TKK), Finland Eero Hyvönen, Helsinki University of Technology (TKK) and University of Helsinki, Finland

Olli Alm, Helsinki University of Technology, Helsinki University of Technology (TKK),

Finland

Chapter VIII A Wiki on the Semantic Web .............................................................................................................. 115 Michel Buffa, Mainline, I3S Lab, France Guillaume Erétéo, Edelweiss, INRIA, France Fabien Gandon, Edelweiss, INRIA, France Chapter IX Personal Knowledge Management with Semantic Technologies ....................................................... 138 Max Völkel, Forschungszentrum Informatik (FZI) Karlsruhe, Germany Sebastian Schaffert, Salzburg Research Forschungsgesellschaft mbH, Austria Eyal Oren, Digital Enterprise Research Institute (DERI), Ireland Chapter X DeepaMehta: Another Computer is Possible ...................................................................................... 154 Jörg Richter, DeepaMehta Company, Germany Jurij Poelchau, fx-Institute, Germany

Section III

Methods for Semantic Work Environments

Chapter XI Added-Value: Getting People into Semantic Work Environments ..................................................... 181 Andrea Kohlhase, Jacobs University Bremen and DFKI Bremen, Germany Normen Müller, Jacobs University Bremen, Germany Chapter XII Enabling Learning on Demand in Semantic Work Environments: The Learning in Process Approach ..................................................................................................... 202 Andreas Schmidt, FZI Research Center for Information Technologies, Germany

Section IV

Techniques for Semantic Work Environments

Chapter XIII Automatic Acquisition of Semantics from Text for Semantic Work Environments ............................................................................................................................ 217 Maria Ruiz-Casado, Universidad Autonoma de Madrid, Spain Enrique Alfonseca, Universidad Autonoma de Madrid, Spain Pablo Castells, Universidad Autonoma de Madrid, Spain Chapter XIV Technologies for Semantic Project-Driven Work Environments ........................................................ 245 Bernhard Schandl, University of Vienna, Austria Ross King, Austrian Research Centers GmbH (ARC) Research Studios, Austria Niko Popitsch, Austrian Research Centers GmbH (ARC) Research Studios, Austria Brigitte Rauter, P.Solutions Informationstechnologie GmbH, Austria Martin Povazay, P.Solutions Informationstechnologie GmbH, Austria Chapter XV An Integrated Formal Approach to Semantic Work Environments Design ............................................................................................................................................... 262 Hai H. Wang, University of Southampton, UK Jin Song Dong, National University of Singapore, Singapore Jing Sun, University of Auckland, New Zealand Terry R. Payne, University of Southampton, UK Nicholas Gibbins, University of Southampton, UK Yuan Fang Li, National University of Singapore, Singapore Jeff Pan, University of Aberdeen, UK

Compilation of References .............................................................................................................. 313

About the Contributors ................................................................................................................... 337

Index ................................................................................................................................................ 346

Detailed Table of Contents

Foreword ............................................................................................................................................ xiv

Preface ................................................................................................................................................ xvi

Acknowledgment ..............................................................................................................................xxiii

Section I

Introduction

This section will help the reader to learn about the most common technologies and to be able to classify these technologies. In addition, the reader will get a better understanding of why certain decisions about the usage of technologies have been made in the chapters of the subsequent sections. These chapters give an introduction to technologies that can be used to develop semantic work environments (SWE) and present several R&D projects in which different technologies and related tools have been developed. The authors compare these technologies using characteristics such as collaboration, communication, and so forth, and provide the reader with an overview of fundamental building blocks as well as development requirements for SWE development.

ments rely on: Web 2.0 and the Semantic Web. Both approaches aim at integrating distributed data and information to provide enhanced search, ranking, browsing, and navigation facilities for SWEs. They present several research projects to show how both fields can lead to synergies for developing knowledge bases for the Semantic Web.

Chapter II Communication Systems for Semantic Work Environments ................................................................ 16 Thomas Franz, University of Koblenz-Landau, Germany Sergej Sizov, University of Koblenz-Landau, Germany Thomas Franz and Sergej Sizov point out that communication is one of the main tasks of a knowledge

worker, as it denotes the exchange of information and the transfer of knowledge, making it vital for any collaborative human work. The authors introduce different communication systems to indicate their different utilization and role in knowledge work. They present requirements on communication for SWEs and compare conventional communication tools and channels with these requirements. After presenting research work that contributes to the communication of knowledge work, they conclude with a visionary scenario about communication tools for future SWEs.

or Socially Enabled Semantic Web? ..................................................................................................... 33

Sebastian Schaffert, Salzburg Research Forschungsgesellschaft, Austria

Sebastian Schaffert continues the discussion of the synergies between Web 2.0/social web and the Se- mantic Web. He introduces two perspectives on how Semantic Social Software can be reached: One perspective is semantically enabled social software, that is, the usage of semantic metadata to enhance existing social software. The other perspective is a socially enabled Semantic Web, which means the usage of Social Software to create semantic metadata. Three examplary applications of semantic social software (i.e., Semantic Wikis, Semantic Weblogs, and e-portfolios) are provided by the author for deriving outstanding aspects of Semantic Social Software.

Section II

Semantic Work Environment Tools

This section provides seven chapters that are more related to concrete realizations of SWEs—tools developed to support work environments and personal activities using semantic technologies. These tools come from very different application domains such as oil drilling, journalism, and library help desk services, and motivate many application scenarios that exist for semantic work environments. The chapters further extend the overview of technologies already provided in Section I. Concrete architectures and platforms are presented for developing SWEs such as Semantic Wikis, Semantic Personal Knowledge Management systems, and Semantic Desktops. Several chapters also elaborate on the topics of authoring and annotating content, refer to inference technologies such as case-based reasoning, or present visualization approaches to support the tagging, linking, or presentation of content in SWEs.

editing, and browsing mathematical knowledge. In this Wiki, the regular Wiki markup is replaced by a

markup format and ontology language for mathematical documents. SW M represents a social semantic work environment, which facilitates the creation of a shared collection of mathematical knowledge.

Damaris Fuentes Lorenzo, Juan Miguel Gómez, and Ángel García Crespo describe a semantic work environment for the collaborative creation of news articles, thus building a basis for citizen journalism. Articles “within” this Wiki can be annotated using ontological metadata. This metadata is then used to reward users in terms of advanced browsing and searching the newspapers and newspaper archives, in particular finding similar articles. Faceted metadata and graphical visualizations help the user to find more accurate information and semantic related data when it is needed. The authors state that the Wiki architecture is domain-independent and can be used for other domains apart from news publishing.

Chapter VI Improved Experience Transfer by Semantic Work Support ................................................................. 84 Roar Fjellheim, Computas AS, Norway David Norheim, Computas AS, Norway Roar Fjellheim and David Norheim describe the Active Knowledge Support for Integrated Operations

(AKSIO) system that supports the experience transfer in operations of offshore oilfields. AKSIO is an example of a SWE that provides information in a timely and context-aware manner. Experience reports are processed and annotated by experts and linked to various resources and specialized knowledge networks. The authors demonstrate how Semantic Web technology is an effective enabler of improved knowledge management processes in corporate environments.

for a Library Help Desk Service ......................................................................................................... 100

Antti Vehviläinen, Helsinki University of Technology (TKK), Finland Eero Hyvönen, Helsinki University of Technology (TKK) and University of Helsinki, Finland

Olli Alm, Helsinki University of Technology, Helsinki University of Technology (TKK), Finland Antti Vehviläinen, Eero Hyvönen, and Olli Alm discuss how knowledge technologies can be utilized in creating help desk services on the Semantic Web. The authors focus on support for the semi-automatic annotation of natural language text for annotating question-answer pairs, and case-based reasoning techniques for finding similar questions. To provide answers matching with the content indexer’s and end-user’s information needs, methods for combining case-based reasoning with semantic search, linking, and authoring are proposed. The system itself is used as a help-desk application in Finnish libraries to answer questions asked by library users.

addresses several social and usability problems of conventional Wikis by combining a WYSIWYG editor and semantic annotations. SweetWiki makes use of semantic web concepts and languages and demonstrates how the use of such paradigms can improve navigation, search, and usability by preserving the essence of a Wiki: simplicity and social dimension. In their chapter, they also provide an overview of several other semantic Wikis.

Chapter IX Personal Knowledge Management with Semantic Technologies ....................................................... 138 Max Völkel, Forschungszentrum Informatik (FZI) Karlsruhe, Germany Sebastian Schaffert, Salzburg Research Forschungsgesellschaft mbH, Austria Eyal Oren, Digital Enterprise Research Institute (DERI), Ireland Max Völkel, Sebastian Schaffert, and Eyal Oren present how to use semantic technologies for improv-

ing one’s personal knowledge management. Requirements on personal knowledge management based on a literature survey are provided. Current nonsemantically as well as semantically-enhanced personal knowledge management tools were investigated and the reader is provided with an overview of existing tools. To overcome the drawbacks of the current systems, semantic Wikis are presented as the best implementation of the semantically-enhanced personal knowledge management vision—even if they do not perfectly fulfill all the stated requirements.

Chapter X DeepaMehta: Another Computer is Possible ...................................................................................... 154 Jörg Richter, DeepaMehta Company, Germany Jurij Poelchau, fx-Institute, Germany Jörg Richter and Jurij Poelchau present the DeepaMehta platform as a semantic work environment. This

platform replaces the traditional desktop by a semantic desktop. The authors explain the multilayered distributed architecture of DeepaMehta, which provides native support for topic maps to visualize the underlying semantics of knowledge. Two exemplary applications of the DeepaMehta platform are presented that implement semantic work environments. The authors conclude their chapter with interesting future research directions and open questions that reflect future applications of SWEs.

Section III

Methods for Semantic Work Environments

Besides defining the requirements and choosing the right building blocks for developing an SWE, the success of such an environment still depends first of all on how the systems motivate people to participate and use the system, and second, on how information is structured and presented to the user. Hence, this section describes methods for better involving people in Semantic Work Environments and for enhanc- ing so-called context-steered learning in these environments.

mantic work environments. They argue that the underlying motivational problem between vast semantic potential and extra personal investment can be analyzed in terms of the “Semantic Prisoner’s Dilemma.” Based on these considerations, they describe their approach of an added-value analysis as a design method for involving people in Semantic Work Environments. In addition, they provide an overview of other software design methods that can be used to develop SWEs and present two application examples of this analysis approach.

Chapter XII Enabling Learning on Demand in Semantic Work Environments: The Learning in Process Approach ..................................................................................................... 202 Andreas Schmidt, FZI Research Center for Information Technologies, Germany Andreas Schmidt presents a method for building individual e-learning material that can be presented in SWEs. The cornerstone of this approach is the context-steered learning method, which uses the context of

users and ontologically enriched learning material to build tailored e-learning material. Context-steered learning implements pedagogical guidance and thus goes beyond simple information delivery. It considers not only the current learning needs, but also the prerequisites for understanding the provided resources and a limited form of meaningful order (in the pedagogical sense). The author uses an architecture of loosely coupled services for implementing context-steered learning. This chapter is a contribution towards the challenge of presenting and structuring information so that it supports short-term problem solving as well as long-term competence development.

Section IV

Techniques for Semantic Work Environments

In order to realize Semantic Work Environments, information has to be collected, structured, and processed. This section describes specific techniques for supporting these activities, which might be helpful when building one’s own semantic-based tools. These techniques enhance available techniques and therefore provide better solutions for the challenges of extracting semantics, managing information from various distributed sources, and developing interfaces to quickly manage, annotate, and retrieve information.

automatically extracting semantics from natural language text documents. These techniques can be used to support the semantic enrichment of plain information, since the manual tagging of huge amounts of contents is very costly. They describe how natural language processing works in general and state methods for tackling the problem of “Word Sense Disambiguation.” The authors provide a set of techniques for information and relationship extraction. This chapter gives a comprehensive overview of semantic acquisition techniques for SWEs, which reduce the cost of manually annotating preexisting information.

Chapter XIV Technologies for Semantic Project-Driven Work Environments ........................................................ 245 Bernhard Schandl, University of Vienna, Austria Ross King, Austrian Research Centers GmbH (ARC) Research Studios, Austria Niko Popitsch, Austrian Research Centers GmbH (ARC) Research Studios, Austria Brigitte Rauter, P.Solutions Informationstechnologie GmbH, Austria Martin Povazay, P.Solutions Informationstechnologie GmbH, Austria Bernhard Schandl, Ross King, Niko Popitsch, Brigitte Rauter, and Martin Povazay state that capturing

the semantics of documents and their interrelations supports finding, exploring, reusing, and exchanging digital documents. They believe that the process of capturing semantics must take place when the system users have maximum knowledge about a certain document (i.e., when the document is created or updated) and should interfere with a user’s normal workflow as little as possible. Therefore, they present METIS, a framework for the management of multimedia data and metadata from various distributed sources; Ylvi, a semantic Wiki platform with a high-level, collaborative user interface built on top of METIS for rapid knowledge exchange and management; and SemDAV, a Semantic-Web-based proto- col that allows integrating personal information and sharing semantic information. SemDAV provides interfaces to quickly manage, annotate, and retrieve information.

Chapter XV An Integrated Formal Approach to Semantic Work Environments Design ............................................................................................................................................... 262 Hai H. Wang, University of Southampton, UK Jin Song Dong, National University of Singapore, Singapore Jing Sun, University of Auckland, New Zealand Terry R. Payne, University of Southampton, UK Nicholas Gibbins, University of Southampton, UK Yuan Fang Li, National University of Singapore, Singapore Jeff Pan, University of Aberdeen, UK The authors state that the services found in SWEs may have intricate data states, complex process behav-

iors, and concurrent interactions. They propose TCOZ (Timed Communicating Object-Z), a high-level design technique, as an effective way for modeling such complex SWE applications. Tools for mapping those models, for example, to the Unified Modeling Language (UML) or to several other formats, have been developed. In this chapter, the authors explain TCOZ, and use TCOZ for formally specifying the functionalities of an examplary application (a talk discovery system). They present tools for extracting an OWL web ontology used by software services as well as for extracting the semantic markup for software services from the TCOZ design model automatically.

Chapter XVI Lightweight Data Modeling in RDF ................................................................................................... 281 Axel Rauschmayer, University of Munich, Germany Malte Kiesel, DFKI, Germany Axel Rauschmayer and Malte Kiesel state that the RDF standard is, in fact, suitable for lightweight data

modeling, but it lacks clearly defined standards to completely support it. They present the Editing Meta- Model (EMM), which provides standards and techniques for implementing RDF editing: It defines an RDF vocabulary for editing and clearly specifies the semantics of this vocabulary. The authors describe the EMM constructs and its three layers (i.e., schema, presentation, and editing). The schema defines the structure of the data, the presentation selects what data to display, and the editing layer uses projections to encode, visualize, and apply changes to RDF data. Particular focus is given to a formal description of the EMM and to the potential implementation of this model in the GUI of a semantic work environment. At the end of the chapter they provide a set of related technologies for modeling semantics for SWEs. They think that EMM is useful for developers of data-centric (as opposed to ontology-centric) editors and can serve as a contribution to the ongoing discussion about simpler versions of OWL.

Compilation of References .............................................................................................................. 313

About the Contributors ................................................................................................................... 337

Index ................................................................................................................................................ 346

xiv

Foreword

Since the dawn of the Semantic Web, we have been working on developing techniques that use the data, metadata, and links available on the World Wide Web (WWW) for inferring additional services. These services aim at supporting our work and lives with technologies such as the resource description framework (RDF) and, most recently, the Web ontology language (OWL). Several of these technologies enable or use semantic data and also enable further technologies that exploit the wealth of information on the WWW.

This book, edited by Jörg Rech, Eric Ras, and Björn Decker, deals with another interesting and important problem, namely, integrating semantic technologies into work environments. It looks at ways of creating semantically richer applications that intelligently assist the user with additional information. A richer representation enables new services for people and enables further technologies that exploit this semantic information.

Today, semantic technologies increasingly find their way into collaborative tools such as Wikis, Desk- tops, or Web-based platforms. In the context of corporate settings, these semantic-based collaborative applications represent enhanced tools that intelligently and autonomously support the knowledge worker with relevant information on time. Semantic work environments such as Semantic Wikis, Semantic Desktops, or Web-based semantic platforms are information systems that use semantic technologies to enhance the content in these systems for presentation, querying, reporting, or analysis purposes. Besides the information available on the WWW, these environments raise and exploit the more specific information available throughout company networks that is ripe to be integrated into new services. Furthermore, most employees of these companies like to share their knowledge and use these systems for documenting, storing, and disseminating their knowledge.

To integrate the data into company networks, several systems have been developed that integrate semantic technologies—many of them are presented in this book. The first part of this book (sections one and two) is an interesting collection of chapters dealing with integrating semantic technologies and metadata into work environments. While the first three chapters investigate how semantic collaboration can be enabled and fostered, the other chapters describe real-world semantic work environments such as:

SWiM: A Semantic Wiki for collaboratively building, editing, and browsing mathematical knowl-

edge in order to support knowledge management for mathematicians.
CoolWikNews: A Semantic Wiki devoted to news publishing in order to support knowledge man- agement for journalists.

AKSIO: An active socio-technical system for knowledge transfer between drilling projects, using

documented experiences, best practices, and expert references.

Opas: A semi-automatic annotation and authoring tool to support librarians via specialized help • desk services. SweetWiki: A Semantic Wiki that integrates several semantic technologies to provide a Semantic • Web application platform for everyone. xv SemperWiki: A Semantic Wiki that is targeted to support personal knowledge management with

semantic technologies.

DeepaMehta: A platform designed to provide knowledge workers with additional information that • supports their work, thoughts, and collaborations with colleagues. Ylvi: A Semantic Wiki that enables and supports the creation of semantic information during normal

project work.

OntoWiki: A Semantic Wiki aimed to support the social and semantic collaboration. •

In order to enable and keep these semantic work environments alive, we need several technologies and methodologies. Standard data modeling formats and methods are necessary for promoting interop- erability and for integrating users into these systems. This issue of using techniques and methods for semantic work environments is addressed in the second part (sections three and four) of this book. The six chapters address the following questions:

How can we integrate people into semantic work environments and show them the added value • these systems offer? How can we enable and foster learning during work activities and on demand in semantic work • environments? How can we automatically acquire semantic information from previously existing sources for • semantic work environments? How can we integrate the various existing technologies for semantic work environments to support • project-driven work? How can we model the data, metadata, and relations used in semantic work environments? •

In summary, the editors have selected a very interesting collection of chapters that present the current state of the art in semantic work environments. The primary objective of this book is to mobilize researchers and practitioners to develop and improve today’s work environments using semantic technologies. It raises the awareness in the research community for the great potential of SWE research. All in all, this book is a significant collection of contributions on the progress in semantic work environments and its use in various application domains. These contributions constitute a remarkable reference for researchers on new topics on the design and operation as well as on technical, managerial, behavioral, and organizational aspects of semantic work environments.

Prof. Dr. Klaus-Dieter Althoff Intelligent Information Systems University of Hildesheim, Germany September 2007

Klaus-Dieter Althoff is full professor at the University of Hildesheim and is directing a research group on intelligent informa-

tion systems. He studied mathematics with a focus on expert systems at the University of Technology at Aachen. In 1992 he

finished his doctoral dissertation on an architecture for knowledge-based technical diagnosis at the University of Kaiserslautern,

where he also received the postdoctoral degree (Habilitation) with a thesis on the evaluation of case-based reasoning systems

in 1997. He worked at the Fraunhofer Institute for Experimental Software Engineering as group leader and department head

until he went to Hildesheim in April 2004. His main interests include techniques, methods and tools for developing, operating,

evaluating, and maintaining knowledge-based systems, with a focus on case-based reasoning, agent technology, experience management, and machine learning. xvi Preface

In many companies, technical work environments integrate information systems aimed at supporting their long term organizational strategy and at providing efficient support to their core business processes

To support the knowledge worker by integrating these information systems is a complex task which requires the participation of various groups of people and technical systems. With the rise of semantic technologies, more and more information gets enriched with semantic metadata, which makes the information ready for harvesting. In the Web 2.0 (Murugesan, 2007) and Web 3.0 (Lassila & Hendler, 2007) movement, we experience this phenomenon through so-called “mashups” (Ankolekar, Krötzsch, Tran, & Vrandecic, 2007) of existing information sources such as search engines (e.g., Google Search), geographical map servers (e.g., Google Maps), collaborative encyclopedias (e.g., Wikipedia), or open picture repositories (e.g., Flickr).

In order to map this phenomenon to the work environments in companies, we have to integrate the different information sources available in and near organizations. Semantic Work Environments (SWE) such as Semantic Wikis (Semantic Wikis, 2005; Völkel, Schaffert, Pasaru-Bontas, & Auer, 2006) or Semantic Desktops (Decker, Park, Quan, & Sauermann, 2005) are aimed at exploiting this wealth of information in order to intelligently assist our daily work. Ideally, they are built to collect data for deriving our current information needs in a specific situation and to provide processed and improved information that can be integrated into the task at hand. Furthermore, as the usage of this information is tightly integrated into our daily work, we do not only take part in the (re)use but also in the creation and sharing of information. This continuous flow of information, experience, and knowledge helps to keep us up-to-date in our area of expertise and enables us to integrate the experience of our colleagues into our own work. Hence, semantic work environments will also address the challenge of life-long learning because they provide easy and fast access to information that fits our current working situation. This means, on the one hand, that such systems help us to solve short-term problems, and on the other hand, that they enhance long-term competence development.

Semantic Work Environments combine the strengths of Semantic Web technologies, workplace applications, and collaborative working—typically for a specific application domain such as research or journalism—and represent the “Semantic Web in the small.” Instead of making all content in the In- ternet machine-readable (i.e., “Semantic Web in the large”), the SWE approach tackles the problem on a smaller, more focused scale. Take Semantic Wikis as an example: Wikis are enhanced by the simple annotation of Wiki content with additional machine-readable metadata and tools that support authors during the writing of new or the changing of existing content (e.g., via self-explaining templates). This approach of building up the Semantic Web in the small is in line with current developments in the area of the Semantic Web. One prominent example is the definition of so called “microformats” (Ayers, 2006; Khare, 2006): Based on standard Web technology, they allow embedding small information chunks like contact information into Web sites. xvii

We believe that semantic work environments are the first step towards achieving the vision of the Se- mantic Web, for several reasons: they are lightweight, goal-oriented, and more likely to use synergies. Semantic work environments are lightweight, since they support a specific problem and, therefore, require only relevant features for this task. They do not intend to solve a general, somewhat unfocused and fuzzy problem but have a certain application domain that imposes specific problem types to be solved.

Therefore, requirements elicitation and implementation of the semantic work environments can be performed in a goal-oriented way and can be related to a set of working situations with specific tasks, technical work applications, and networks of people. Since they operate within a defined organizational boundary or community, reaching a consensus about the needed concepts and their meaning (e.g., by creating a consensus through an ontology) can be performed more easily compared to general Semantic Web applications. In addition, due to this focus, a quick return on investment is more likely.

The focus of SWEs is also the basis for synergies that arise from embedding them tightly into the business processes and workflows within an organization. These business processes provide relevant information for classifying and organizing the information created and reused. This information can later be exploited by inference techniques to improve reuse by people operating in similar contexts. A second aspect of synergies is to overcome the dichotomy between the need for information and the often insufficient willingness to make information available for others.

SWEs will play an important role for information storage, acquisition, and processing in specific application domains during knowledge work. In the future, they will enable the widespread use of automated inference mechanisms or software agents on top of the semantic information. Semantic enrichment of work environments will help participants in their daily work to avoid risks and project failures that are frequently encountered in traditional projects.

CHALLENGES

A commonly accepted fact is the ever-increasing amount of information we have to cope with during our daily work. While a century ago, most countries were based on manual-labor cultures, we are currently living in a world of knowledge workers. And the rise of computers and their integration into our daily work environments increases this flood of information even more. Or, to quote John Naisbitt: “We are drowning in information but starved for knowledge” (Naisbitt, 1984).

Therefore, we need approaches to reduce the amount of information and to optimize access to important information and the way it is presented to the user—anywhere and anytime. Approaches such as Wikis are important; however, there is still much work to be done to integrate them into our daily working environments.

Attempts to construct semantic work environments have to adequately deal with the challenges that exist in the new millennium. Such challenges can be classified into several categories:

Challenge 1: Enabling the collaboration of work communities for exchanging information and

using semantic work environments.

Challenge 2: Building semantic work environments to support social collaboration, information • integration, and automated inference. Challenge 3: Starting semantic work environments and keeping them alive. • Challenge 4: Adequately presenting information to a user so that it supports the two extremes of

short-term problem solving and long-term competence development.

xviii Table 1. Chapters and approached challenges

Chapter Challenge 1 Challenge 2 Challenge 3 Challenge 4 Challenge 5 Challenge 6 Challenge 7 Challenge 8

Chapter I

 

Chapter II



Chapter III

 

Chapter IV



Chapter V

   

Chapter VI

  

Chapter VII

 

Chapter VIII

   

Chapter IX



Chapter X

  

Chapter XI



Chapter XII

 

Chapter XIII



Chapter XIV



Chapter XV



Chapter XVI



Challenge 5: Coping with the plethora of overlapping and similar Semantic Web-technologies, that

is, how to select the right building blocks for the development of semantic work environments.

Challenge 6: Coping with quick innovation cycles and the resulting time pressure that drives us • away from classical search to context-sensitive and pro-active information offerings. Challenge 7: Obtaining the needed information in a timely manner.

Challenge 8: Building architectures of such environments with different APIs, data structures,
and business processes. In order to deal with the complexity of developing such tools, adequate methodologies, technologies, and ontologies are mandatory.

As in the case of Chapter X, most chapters in this book do not only approach one challenge, but tackle several of them.

Today, members from multiple disciplines work on SWEs and collaborate to provide highly integrated services by integrating the ever increasing amount of information. Based on collaborative technologies such as Wikis and using semantic technologies such as OWL, collaborative semantic work environments xix

IGI Global Emerging Technologies For Semantic Work Environments Techniques Methods And Applications Jun 2008 ISBN 1599048779 pdf

Foreword

In many companies, technical work environments integrate information systems aimed at supporting their long term organizational strategy and at providing efficient support to their core business processes

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Dukungan

Links

IGI Global Emerging Technologies For Semantic Work Environments Techniques Methods And Applications Jun 2008 ISBN 1599048779 pdf

Foreword

In many companies, technical work environments integrate information systems aimed at supporting their long term organizational strategy and at providing efficient support to their core business processes

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