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
Hershey • New York
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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
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book set is original material. The views expressed in this book are those of the authors, but not necessarily of the publisher.
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
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
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
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 environ-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 dif-ferent 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 Se-mantic Web. He introduces two perspectives on how SeSe-mantic 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 de-riving outstanding aspects of Semantic Social Software.
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
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
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, link -ing, 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 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 exist-ing 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
underlying semantics of knowledge. Two exemplary applications of the DeepaMehta platform are pre-sented 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 learnenhanc-ing 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 se-mantic work environments. They argue that the underlying motivational problem between vast sese-mantic 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
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 semi-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 ac-quisition 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 exchang -ing digital documents. They believe that the process of captur-ing 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
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 extract-ing an OWL web ontology used by software services as well as for extractextract-ing 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
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 im-portant 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 informa -tion 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 knowlknowl-edge 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.
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 cur-rent 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 technolo -gies. 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
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 seman-tic technologies, more and more information gets enriched with semanseman-tic 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;
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 ap -plication 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 im-portant 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.
xviii
• 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
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
xix
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 under-standable 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 lan-guages, 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 informa-tion to the user. How should the informainforma-tion be structured? How should it be presented? What kind of navigation support should be offered? Information might be gathered from very different sources, dif-ferent 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 bring
-ing together various research studies from different subfields and underlin-ing 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.
xx
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 se-mantic 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 Opera-tions (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 man -agement. 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 Dee-paMehta 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
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-learn-ing material.
Base techniques for building Semantic Work Environments are presented in the final section. Chap -ter 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 discov -ery 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
Ankolekar, A., Krötzsch, M., Tran, T., & Vrandecic, D. (2007). The two cultures: Mashing up Web 2.0 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 http://www.w3.org/ 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 http://www.w3.org/TR/ 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.
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Decker, S., Park, J., Quan, D., & Sauermann, L. (2005, November 6). The semantic desktop - next gen-eration information management and collaboration infrastrucutre. Paper presented at the International Semantic Web Conference (ISWC 2005), Galway, Ireland.
Khare, R. (2006). Microformats: The next (small) thing on the Semantic Web? Internet Computing, IEEE, 10(1), 68-75.
Lassila, O., & Hendler, J. (2007). Embracing“Web 3.0.” IEEE Internet Computing, 11(3), 90-93. McClelland, M. (2003). Metadata standards for educational resources. Computer, 36(11), 107-109. Murugesan, S. (2007). Understanding Web 2.0. IT Professional, 9(4), 34-41.
Naisbitt, J. (1984). Megatrends: Ten new directions transforming our lives. New York: Warner Books. Oberle, D., Ankolekar, A., Hitzler, P., Cimiano, P., Sintek, M., Kiesel, M., et al. (2007). DOLCE ergo SUMO: On foundational and domain models in the SmartWeb integrated ontology (SWIntO). Web Semantics, 5(3), 156-174.
Pease, A. (2003). SUMO: A sharable knowledge resource with linguistic inter-operability. Paper presented at the Natural Language Processing and Knowledge Engineering, 2003. Proceedings. 2003 International Conference on.
Semantic Wikis. (2005). Semantic Wiki Overview. Retrieved March 13, 2008, from http://c2.com/cgi/ wiki?SemanticWikiWikiWeb
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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 apprecia-tion 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 Thomp-son, 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
Section I
Chapter I
Enabling Social
Semantic Collaboration:
Bridging the Gap Between Web 2.0
and the Semantic Web
Sören Auer
University of Pennsylvania, USA
Zachary G. Ives
University of Pennsylvania, USA
Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
INTRODUCTION
The concepts Social Software and Web 2.0 were coined to characterize a variety of (sometimes minimalist) services on the Web, which rely on social interactions to determine additions, annota-tions, or corrections from a multitude of potentially
minor user contributions. Nonprofit, collabora-tion-centered projects such as the free encyclope-dia Wikipeencyclope-dia belong to this class of services, as well as commercial applications that enable users to publish, classify, rate, and review objects of a certain content type. Examples for this class of
Enabling Social Semantic Collaboration
by making participation and contribution as easy and rewarding as possible.
Even before Social Software and Web 2.0 applications emerged, prior attempts had been made to enable rapid assembly of data on the Web into more informative content: the most well-known such project is the Semantic Web, although researchers had been working on “infor-mation integration for the Web” for many years prior (Mediators,TSIMMIS,Ariadne), with very different methodologies but a similar end goal. The Semantic Web is conceived as an extension of the existing Web to enable machine reason-ing and inference: a prerequisite to this is that
“information is given well-defined meaning”
(Berners-Lee, Hendler, & Lassila, 2001). This approach is based on a standardized description model, Resource Description Framework (RDF) (Lassila & Swick, 1999) and semantic layers on top for semantic nets and taxonomies (RDF-Schema) as well as ontologies, logic axioms, and rules (OWL and SWRL). However, the Semantic Web is not ubiquitous to this point, in part because of the high level of effort involved in annotating data and developing knowledge bases to support the Semantic Web.
The Web 2.0 and Semantic Web efforts, which have largely gone on simultaneously, pose an interesting study in contrasting methods to achieve a similar goal. Both approaches aim at integrating dispersed data and information to provide enhanced search, raking, browsing, and navigation facilities for the Web. However, Web 2.0 mainly relies on aggregate human interpretation (the collaborative “ant” intelligence of community members) as the basis of its metadata creation,
conflict resolution, ranking, and refinement; the
Semantic Web relies on complex but sophisticated knowledge representation languages and machine inference (Table 1). A natural question to ask is whether the different approaches can be combined in a way that leads to synergies. We discuss in this chapter how the question is being answered in the
affirmative by a number of promising research
projects. The main goal of these projects is to support collaborative knowledge engineering in social networks, with high reward and little effort. After presenting fundamental communication and collaboration patterns of Social Software, we exhibit the tool OntoWiki for social, semantic collaboration. In subsequent sections we suggest strategies for employing Social Software and Web 2.0 methods to support the creation of knowledge bases for the Semantic Web. We give an overview on further and relater work and conclude with remarks concerning future challenges.
SOCIAL SOFTWARE AND WEB 2.0
The concepts social software (Webb, 2004) and
Web 2.0 (O’Reilly, 2005) were recently conceived
to explain the phenomenon that computers and technology are becoming more and more impor-tant for human communication and collaboration. In particular the following aspects are important with respect to software enabling social collabora-tion: (1) usability, (2) community and participation, (3) economic aspects, (4) standardisation, and (5) reusability and convergence. In addition to that, a precise delimitation of the concept social software is due to heterogeneity of applications, applicants, and application domains complex.
It was proposed by Shirky (2003) to define the
concept of social software not just with respect
Table 1. Similarities and differences between social software and the Semantic Web
Social Software & Web 2.0 Semantic Web
Collaboration and integration focused Based on the Web
Enabling Social Semantic Collaboration
to characteristics of a certain software, but also with regard to communication patterns leading to the formation of a virtual community. Typical communication patterns of Social Software are depicted in Table 2.
On the technological side, the popularity of so-cial software is related to the development and use of the software development and communication paradigms AJAX (Asyncrounous JAvascript and XML), REST (Representational State Transfer), and JSON (Javascript Object Notation). These, in comparison to their counterparts Web services, RPC or remote desktop light-weight technologies enable completely new adaptive and interactive application architectures and services.
Based on these technologies, a number of methods for user-interaction established, which encourage and simplify spontaneous tions, help to organize a multiplicity of contribu-tions, as well as to syndicate and mutually integrate the gained data. These include:
• Folksonomies:Content annotation by means of tags (i.e., self-describing attributes at-tached to content objects)enable the fuzzy but intuitive organization of comprehensive content bases (Golder et al., 2006). Tag clouds visualize tags to support navigation
and filtering. Tags are colocated in a tag
cloud when jointly used and emphasized differently to stress their usage frequency.
• Architecture of participation: Already the usage of an application creates an added value. For example, the added value can be generated by interactively evaluating us-age statistics to determine popular content objects or by collecting ratings from users to classify content with respect to quality. • Instant-gratification: Active users are
re-warded with enhanced functionality and their reputation in the user community is visibly increased. This promotes contributions and helps to establish a collaboration culture. • Mashups and feeds: The content collected
in the system is syndicated for other services (e.g., RSS feeds, JSON exports, or public APIs). This allows seamless integration of different data end transforms the Web into a Service Oriented Architecture.
In the remainder of this chapter, we suggest approaches how these Web 2.0 and Social Soft-ware methods can be adopted to support semantic collaboration scenarios.
SOCIAL SEMANTIC
WORK ENVIRONMENTS
Recently, a number of strategies, approaches, and applications emerged aiming at employing elements of the Web 2.0 and Social Software for
Table 2. Typical communication patterns for social software
Pattern Name Partner Direction Example
Point-to-point 1:1 E-mail, SMS/MMS
Bidirectional 1:1 IM, VoIP
Star-like 1:n Web pages, Blogs, Podcasts
Enabling Social Semantic Collaboration
semantic collaboration on the Web. Examples are the approaches to integrate semantics into wikis,
to bring the semantic to the user’s desktops, or
to weave social networks by means of semantic technologies such as FOAF. The application do-main for semantic collaboration scenarios can be often characterized in the following way:
• A single, precise usage scenario of the envisioned knowledge bases is initially not
known or (easily) definable.
• A possibly large number of involved actors is spatially separated.
• The collaboration is not a business in itself but a means to an end.
• Only a small amount of human and financial
resources is available.
• Application of reasoning services is (initially) not mission critical.
• The collaboration environment is Web-centric.
Some concrete examples for the growing number of in such a way characterized usage scenarios of Social Semantic Working Environ-ments (SSWE) are summarized in Table 3.
In order to organize the collaboration in SS-WEs we collect in the remainder of this section some requirements for SSWE tool support. The main goal is to rapidly simplify the acquisition, presentation and syndication of semantically structured information (e.g., instance data) from and for end users. This can be achieved by regard-ing knowledge bases as “information maps.” Each node at the information map is represented
visu-ally and intuitively for end users in a generic but
configurable way and interlinked to related digital
resources. Users should be enabled to enhance the knowledge schema incrementally as well as to contribute instance data agreeing on it as easy as possible to provide more detailed descriptions
and modelings. More specifically, the following
components should be realized in SSWEs which follow the star-like communication pattern:
• Intuitive display and editing of instance data should be provided in generic ways,
yet enabling means for domains specific
extensions.
• Semantic views allow the generation of different views and aggregations of the knowledge base.
• Versioning and evolution provides the op-portunity to track, review, and selectively roll-back any changes made.
• Semantic search facilitates keyword searches on all information, search results
can be filtered and sorted (using semantic
relations).
• Community support enables discussions about small information chunks. Users are encouraged to vote about distinct facts or prospective changes.
• Online statistics interactively measure the popularity of content and activity of users. • Semantic syndication supports the distri-bution of information and their integration into other services and applications.
Table 3. Example SSWE application scenarios
Aim of Semantic
Collaboration Example Domain
Example application
Creation of shared / common terminologies Biomedicine Open Biomedical Ontologies (OBO)
Integration of dispersed information sources Virtual organizations Web sites of research networks, or social and charitable organizations
Enabling Social Semantic Collaboration
In the next sections we propose strategies on how to put these requirements into effect in real systems and provide some examples of a prototypical implementation of an SSWE called OntoWiki.
VISUAL REPRESENTATION
OF SEMANTIC CONTENT
The intuitive visual representation of highly structured and interlinked content is a major challenge on the Semantic Web. The possibilities for adopting strategies from Social Software here are due to the more heterogeneous and complex content on the Semantic Web limited. However, a commonly seen strategy in Social Software such as Wikis and Blogs is to visually represent content bases to users in the shape of “information maps.” Each node at the information map, that is, Blog or Wiki article, is represented as a Web accessible page and interlinked to related nodes. Wiki or Blog article titles are used to create intuitive and recognizable Web addresses to ease navigation in the information map. A similar strategy can be applied for the generic visual representation of Semantic Web knowledge bases—a Web page can be rendered for each knowledge base object compiling all information available about the object and interlinking it with related content.
Different Views on Instance Data
In addition to regarding knowledge bases on the Semantic Web as interlinked “information maps,” the intuitive visual representation can be facilitated by providing different views on instance data. Such views can be either domain
specific or generic. Domain specific views can
be seen in analogy to Web 2.0 Mashups and will
have to be implemented specifically for a certain
application scenario. Generic views, on the other hand, provide visual representations of instance data according to certain property types. We give some examples.
List Views
List views present a selection of several instances in a combined view. The selection of instances to display can be either based on class member-ship (i.e., according to an rdf:type property)
or based on the result of a selection by a facet or full-text search. List views can be made
addition-ally configurable by enabling users to toggle the
display of commonly used properties. Further-more, each list element representing an individual instance should be linked to an individual view of that instance containing all related information.
Individual Views
Individual views combine all the information related to a certain node in the knowledge base, that is, all properties and their values attached to a particular instance. Property values pointing to other individuals are (according to the information map metaphor) rendered as HTML links to the corresponding individual view. Alternatively, to get information about the referenced individual without having to load the complete individual view users can be enabled to expand a short summary (loaded per AJAX) right where the reference is shown.
Map View
Enabling Social Semantic Collaboration
are dynamically fetched from the knowledge base and displayed directly within the map view.
Calendar View
Instances having property values with the associ-ated datatype xsd:date can be displayed in a
calendar view (see Figure 1). As for the map view the selection of instances displayed in the calen-dar view can be the result of a full-text search or
facet-based filtering. Each item displayed can be
linked to the individual view of the corresponding instance. To be able to integrate the calendar data with other Web services or desktop applications, a link can be offered to export calendar items in iCal format.
COLLABORATIVE AUTHORING
Since Social Software and Web 2.0 applications
are mainly focussed on a specific content type,
content authoring functionality is mostly realized
in an application specific way. A common element,
however, is tagging functionality for individually annotating content objects. To enable users to author information within a Semantic Web appli-cation in a generic, appliappli-cation independent way, we see two complementary edit strategies:
Inline Editing
The smallest possible information chunks (i.e., RDF statements) presented on the user interface of the Semantic Web application are editable for users. For example, all information originat-ing from statements and presented on the user interface can be equipped with small edit and add buttons (see Figure 2). On activation of the buttons, a suitable editing widget can be loaded into the currently displayed page and the cor-responding statement object can be edited or a similar content added. This strategy can be seen analogous to the WYSIWYG (What You See Is What You Get) for text editing, since information can be edited in the same environment as it is presented to users.
View Editing
Common combinations of information are editable in one single step. This requires the generation of comprehensive editing forms based on the view to be edited. The same technique as for generating the view can be applied for generating a suitable form, if the display of property values is replaced with appropriate widgets for editing these values. Examples for editable views are forms to add or
edit (a) all information related to a specific in
Enabling Social Semantic Collaboration
several instances. The latter simplifies the addi -tion of informa-tion after a set of instances was initially created.
Both editing strategies are founded on the idea that users edit content exactly at the same place where it is displayed. This facilitates in-cremental additions as well as ease-of-use and promotes user contributions in constructing a knowledge base.
Editing Widgets
The implementation of both strategies can be grounded on a library of editing widgets thus simplifying extensions for new data-types and
domain-specific enhancements. Such widgets
can be implemented in a server side program-ming language; they generate HTML fragments together with appropriate CSS (Cascading Style
Sheet) definitions and optionally JavaScript code. They may be customized for usage in specific
contexts. In Table 4, we propose some semantic
and datatype specific widget types.
Concept Identification and Reuse
Knowledge bases become increasingly
advanta-geous, if once defined concepts (e.g., classes,
properties, or instances) are as much reused and interlinked as possible. This especially eases the task of rearranging, extracting, and aggregating knowledge. To become part of the daily routine
Figure 2. OntoWiki instance display with statement edit buttons (left). Statement editor with interactive search for predefined individuals based on AJAX technology (right)
Table 4. Editing widgets for the construction of edit forms
Semantic widgets Datatype widgets
Statements: allow editing of subject, predicate, and object.
Text editing: include restricted configurations for
e-mail, numbers, and so forth.
Nodes: enable editing of either literals or resources. WYSIWIG HTML editor: edits HTML fragments.
Resources: search and select for/from existing
resources. Dates: selects dates from a calendar.
Literals: literal data in conjunction with datatype/
