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

InformatIon scIence reference
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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.
III. Ras, Eric.
TK5105.88815.E44 2008
658.4'038--dc22
2007042680
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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

Chapter XVI
Lightweight Data Modeling in RDF ................................................................................................... 281
Axel Rauschmayer, University of Munich, Germany
Malte Kiesel, DFKI, Germany

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.
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
Sören Auer and Zachary Ives introduce the interrelation between two trends that semantic work environments 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.
Chapter III
Semantic Social Software: Semantically Enabled Social Software
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 Semantic 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.


Chapter IV
SWiM: A Semantic Wiki for Mathematical Knowledge Management ................................................. 47
Christoph Lange, Jacobs University Bremen, Germany
Michael Kohlhase, Jacobs University Bremen, Germany
Christoph Lange and Michael Kohlhase present SWiM, a semantic Wiki for collaboratively building,
editing, and browsing mathematical knowledge. In this Wiki, the regular Wiki markup is replaced by a
markup format and ontology language for mathematical documents. SWiM represents a social semantic
work environment, which facilitates the creation of a shared collection of mathematical knowledge.
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
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.
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


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.
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
Michel Buffa, Guillaume Erétéo, and Fabian Gandon present a semantic Wiki called SweetWiki that
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 improving 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 enhancing so-called context-steered learning in these 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
Andrea Kohlhase and Normen Müller analyze the motivational aspect of why people are not using semantic 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.
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
Maria Ruiz-Casado, Enrique Alfonseca, and Pablo Castells provide an overview of techniques for semiautomatically 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 protocol 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 behaviors, 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 MetaModel (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, Desktops, 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 knowledge in order to support knowledge management for mathematicians.
CoolWikNews: A Semantic Wiki devoted to news publishing in order to support knowledge management 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 interoperability 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 information 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 Internet 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.


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We believe that semantic work environments are the first step towards achieving the vision of the Semantic 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.


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Challenge 8





















Chapter III



Chapter IV
Chapter V
Chapter VI





Chapter VII





Chapter VIII











Chapter XIII



Chapter XIV

•
•




Chapter XII

•





Chapter XI

•





Chapter IX
Chapter X

Challenge 5

Challenge 4

Challenge 3

Challenge 7

Chapter II

Challenge 6

Chapter I

Challenge 2

Chapter

Challenge 1

Table 1. Chapters and approached challenges

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.

solutions/BaCkground
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


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can be created that are more efficient and effective than the sum of their parts and support the work of
their users. However, this requires coping with different APIs, data structures, business and learning
processes, as well as with the complexity of developing such tools, methodologies, technologies, and
ontologies.
Fortunately, SWEs do not need to be built from scratch. Modern information technologies as well as
developments in knowledge management provide a substantial basis for developing SWEs. In particular,
the vision of the Semantic Web (Berners-Lee, 1998) provides the basis for SWEs: Documents understandable by humans are augmented with machine-processable metadata. The Semantic Web provides
standards such as the resource description framework (RDF) (Decker, Melnik et al., 2000; Decker, Mitra,
& Melnik, 2000) or the Web ontology language (OWL) (Dean et al., 2002). Based on these standard languages, ontologies—that is, formal descriptions of concepts and their relations—allow inferring further
facts and hypotheses. Examples of such ontologies are the document description ontology Dublin Core
(McClelland, 2003) or upper-level ontologies like SUMO (Bouras, Gouvas, & Mentzas, 2007; Pease,
2003) or DOLCE (Oberle et al., 2007). These standards as well as the tools using these standards are
the technical building blocks for semantic work environments.
Besides the usage of such technologies, we have to think about how such systems provide information to the user. How should the information be structured? How should it be presented? What kind of
navigation support should be offered? Information might be gathered from very different sources, different domains, and communities. The semantic annotation of information will help us to select relevant
information and to put these information chunks in relation, thus giving a meaning to the information
set. Solutions for making information more understandable, transferable to a new situation, and more
learnable can be found in the domain of e-learning and knowledge management systems, (educational)
adaptive hypermedia systems, instructional design literature, and so forth.

Book Content
The objective of this book is to provide an overview of the field of semantic work environments by bringing together various research studies from different subfields and underlining the similarities between
the different processes, issues, and approaches. The idea is also to show that many different application
areas can benefit from the exploitation of already existing information sources. In order to present the
solutions that address the challenge of creating semantic work environments by developing adequate
methodologies, technologies, and ontologies, we structured the book into the four sections Introduction,
Tools, Methods, and Techniques.
The introduction section provides approaches that enable collaborative semantic work environments
while the tools section gives an overview of currently implemented technologies with concrete results
from field applications. The methods section provides insights into how to set up and run semantic work

environments, and the techniques section describes base technologies to be used within semantic work
environments.
The introduction section starts with Chapter I, “Enabling Social Semantic Collaboration: Bridging
the Gap between Web 2.0 and the Semantic Web” by Sören Auer and Zachary Ives. This chapter describes the interrelation between two trends that semantic work environments rely on in order to process
existing and develop new knowledge: Web 2.0 as the base technology for human collaboration and the
Semantic Web as the approach to add machine-processable descriptions to this knowledge. The technical
realization is performed using the example of the tool OntoWiki. Chapter II, “Communication Systems
for Semantic Work Environments,” by Thomas Franz and Sergej Sizov, points out how different means


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of communication are used within knowledge work. Common means of communications like e-mail or
groupware are analyzed for “semantic gaps,” which are then refined into requirements for semantically
enabled communication. Chapter III, “Semantic Social Software: Semantically Enabled Social Software
or Socially Enabled Semantic Web?” by Sebastian Schaffert continues the discussion of the synergies
between Web 2.0/social web and the Semantic Web. The author describes two ways of how semantic
social software can be implemented: One possibility is semantically enabled social software, that is, Web
2.0 applications that are enriched with semantics. The other possibility is a Socially Enabled Semantic
Web, which means involving communities in the build-up of ontologies. Three applications provide
examples of semantic social software.
The tools section provides an overview of current applications that can be a part of semantic work
environments. This section comprises chapters four to ten. Chapter IV, “SWIM – A Semantic Wiki for
Mathematical Knowledge Management,” by Christoph Lange and Michael Kohlhase, presents a semantic Wiki to share mathematical knowledge. In this Wiki, the regular Wiki markup is enhanced with
additional mathematical markup, which integrates a mathematical ontology. Chapter V, “CoolWikNews:
More than Meet the Eye in the XXI Century Journalism,” by Damaris Fuentes Lorenzo, Juan Miguel
Gómez, and Ángel García Crespo, is about a semantic work environment for the collaborative creation
of news articles, thus building a basis for citizen journalism. Articles in this Wiki can be annotated using
ontological metadata. This metadata is then used to support navigation within articles, in particular for
finding further relevant articles. Chapter VI, “Improved Experience Transfer by Semantic Work Support,”

by Roar Fjellheim and David Norheim describes, the Active Knowledge Support for Integrated Operations (AKSIO) system. This system supports the experience management of oil drilling activities. This
system supports collaborative knowledge creation and annotation by linking practitioners and experts.
Chapter VII, “A Semi-Automatic Semantic Annotation and Authoring Tool for a Library Help Desk
Service,” by Antti Vehviläinen, Eero Hyvönen, and Olli Alm, provides a help desk system that allows
annotating natural language question-answer pairs with additional semantic information. To support
this annotation, the system suggests potential annotations. Case-based reasoning is then used on this
semantic information to retrieve the best fitting answers to a certain problem. The system itself is used
in a help-desk application run by Finnish libraries to answer questions asked by library users. Chapter
VIII, “A Wiki on the Semantic Web,” by Michel Buffa, Guillaume Erétéo, and Fabian Gandon, is about
the SweetWiki system. This system combines a WYSIWYG editor and semantic annotations, creating
a Wiki system with improved usability. The semantic annotation feature can use previously uploaded
ontologies. In their article, they also provide an overview of several other semantic Wikis. Chapter IX,
“Personal Knowledge Management with Semantic Technologies,” by Max Völkel, Sebastian Schaffert,
and Eyal Oren, presents how to use semantic technologies to improve one’s personal knowledge management. Requirements on personal knowledge management based on a study are described. Current
personal knowledge management tools are investigated concerning their drawbacks. To overcome these
drawbacks, the usage of semantic Wikis for personal knowledge management is suggested. Chapter X,
“DeepaMehta – Another Computer is Possible,” by Jörg Richter and Jurij Poelchau, presents the DeepaMehta platform, which can be used to build up semantic work environments. This platform provides
native support for topics maps to visualize the underlying semantics of knowledge. Two examples of the
application of the DeepaMehta platform show implementations of semantic work environments.
Methods for Semantic Work Environments as the third section of this book presents approaches on
how to build up and run semantic work environments. Chapter XI, “Added Value: Getting People into
Semantic Work Environments,” by Andrea Kohlhase and Normen Müller, analyze the motivational
aspect of why people are using semantic work environments based on the “prisoner’s dilemma.” Based
on these considerations, they describe their approach of added-value analysis. Two application examples


xxi

of this analysis approach are presented. Chapter XII, “Enabling Learning on Demand in Semantic Work
Environments: The Learning in Process Approach,” by Andreas Schmidt, presents a method for building

individual learning material. The cornerstone of this approach is the Context-Steered Learning method,
which uses the context of the user and ontologically enriched learning material to build tailored e-learning material.
Base techniques for building Semantic Work Environments are presented in the final section. Chapter XIII, “Added Automatic Acquisition of Semantics from Text for Semantic Work Environments,”
by Maria Ruiz-Casado, Enrique Alfonseca, and Pablo Castells, provides an overview of techniques for
extracting semantics from text. These techniques can be used to support the semantic enrichment of
previously non-annotated documents. Chapter XIV, “Technologies for Semantic Project-Driven Work
Environments,” by Bernhard Schandl, Ross King, Niko Popitsch, Brigitte Rauter, and Martin Povazay,
is about the METIS media data—an approach to support project management and execution by semantic
work environments. Particular focus is placed on semantically enriched multimedia content. Based on
METIS, the semantic Wiki Ylvi is used to build up organizational memories. Furthermore, the SemDAV
Protocol is used for semantic data exchange. Chapter XV, “An Integrated Formal Approach to Semantic
Work Environments Design,” by Hai H. Wang, Jin Song Dong, and Jing Sun, provides an ontology for
defining Semantic Web services to build up flexible semantic work environments. An online talk discovery system is used as an example of their approach. Finally, Chapter XVI, “Lightweight Data Modeling
in RDF,” by Axel Rauschmayer, and Malte Kiesel, presents the Editing Meta-Model (EMM), which
supports editing within semantic work environments. Particular focus is given to a formal description
of the Editing Meta-Model and to the potential implementation of this model in the GUI of a semantic
work environment.

referenCes
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and the Semantic Web. Banff, Alberta, Canada: ACM Press.
Ayers, D. (2006). The shortest path to the future Web. Internet Computing, IEEE, 10(6), 76-79.
Berners-Lee, T. (1998). Semantic Web roadmap. Retrieved March 14, 2008, from />DesignIssues/Semantic.html
Bouras, A., Gouvas, P., & Mentzas, G. (2007). ENIO: An enterprise application integration ontology.
Paper presented at the 18th International Conference on Database and Expert Systems Applications
(DEXA ’07).
Dean, M., Connolly, D., Harmelen, F. v., Hendler, J., Horrocks, I., McGuinness, D. L., et al. (2002).
OWL Web ontology language 1.0 reference. Retrieved March 13, 2008, from />owl-ref/
Decker, S., Melnik, S., van Harmelen, F., Fensel, D., Klein, M., Broekstra, J., et al. (2000). The Semantic
Web: The roles of XML and RDF. Internet Computing, IEEE, 4(5), 63-73.

Decker, S., Mitra, P., & Melnik, S. (2000). Framework for the Semantic Web: An RDF tutorial. Internet
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Decker, S., Park, J., Quan, D., & Sauermann, L. (2005, November 6). The semantic desktop - next generation information management and collaboration infrastrucutre. Paper presented at the International
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Acknowledgment

Our vision for this book was to gather information about methods, techniques, and applications from

the domain of semantic work environments, to share this information within the community, and to
distribute this information across projects and organizational boundaries.
During the course of realizing this vision, we received much support from people who spent a huge
amount of effort on the creation and review process of the book. We would like to express our appreciation to all the projects and people involved in researching semantic work environments. We are especially
grateful to the authors who provided us with deep insights into their projects and related results.
Furthermore, we are also indebted to the publishing team at IGI Global for their continuing support
throughout the whole publication process. Deep appreciation and gratitude is due to Jessica Thompson, Assistant Managing Development Editor at IGI Global, who supported us and kept the project on
schedule.
Most of the authors of chapters included in this book also served as reviewers for chapters written
by other authors. Thanks go to all those who provided constructive and comprehensive reviews.
Last but not least, thanks also go to the technical staff at Fraunhofer IESE and especially to Sonnhild
Namingha for proofreading parts of the book.
The Editors,
Jörg Rech, Eric Ras, Björn Decker
Kaiserslautern, Germany
September 2007


Section I

Introduction