IGI global emerging technologies for semantic work environments techniques methods and applications jun 2008 ISBN 1599048779 pdf

Se-Section II Semantic Work Environment Tools This section provides seven chapters that are more related to concrete realizations of SWEs—tools veloped to support work environments and p

Emerging Technologies for Semantic Work

Fraunhofer Institute for Experimental Software Engineering, Germany

Hershey • New YorkInformatIon scIence reference

Acquisitions Editor: Kristin Klinger

Development Editor: Kristin Roth

Senior Managing Editor: Jennifer Neidig

Managing Editor: Jamie Snavely

Assistant Managing Editor: Carole Coulson

Copy Editor: Jeannie Porter

Typesetter: Michael Brehm

Cover Design: Lisa Tosheff

Printed at: Yurchak Printing Inc.

Published in the United States of America by

Information Science Reference (an imprint of IGI Global)

701 E Chocolate Avenue, Suite 200

Hershey PA 17033

Tel: 717-533-8845

Fax: 717-533-8661

E-mail: cust@igi-global.com

Web site: http://www.igi-global.com

and in the United Kingdom by

Information Science Reference (an imprint of IGI Global)

Web site: http://www.eurospanbookstore.com

Copyright © 2008 by IGI Global All rights reserved No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher.

Product or company names used in this set are for identification purposes only Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark.

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.

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.

If a library purchased a print copy of this publication, please go to http://www.igi-global.com/agreement for information on activating the library's complimentary electronic access to this publication.

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

Table of Contents

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

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 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

environ-Detailed Table of Contents

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

Se-Section II Semantic Work Environment Tools

This section provides seven chapters that are more related to concrete realizations of SWEs—tools veloped 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 ser-vices, 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 author-ing 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

de-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, 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 ing one’s personal knowledge management Requirements on personal knowledge management based

improv-on a literature survey are provided Current nimprov-onsemantically as well as semantically-enhanced persimprov-onal 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

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 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.

pre-Section III Methods for Semantic Work Environments

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

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

se-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 automatically extracting semantics from natural language text documents These techniques can be used

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

Chapter XV

An Integrated Formal Approach to Semantic Work Environments

Design 262

Hai H Wang, University of Southampton, UK

Jin Song Dong, National University of Singapore, Singapore

Jing Sun, University of Auckland, New Zealand

Terry R Payne, University of Southampton, UK

Nicholas Gibbins, University of Southampton, UK

Yuan Fang Li, National University of Singapore, Singapore

Jeff Pan, University of Aberdeen, UK

The authors state that the services found in SWEs may have intricate data states, complex process 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 extracting the semantic markup for software services from the TCOZ design model automatically

behav-Chapter XVI

Lightweight Data Modeling in RDF 281

Axel Rauschmayer, University of Munich, Germany

Malte Kiesel, DFKI, Germany

Axel Rauschmayer and Malte Kiesel state that the RDF standard is, in fact, suitable for lightweight data modeling, but it lacks clearly defined standards to completely support it They present the Editing Meta-Model (EMM), which provides standards and techniques for implementing RDF editing: It defines an RDF vocabulary for editing and clearly specifies the semantics of this vocabulary The authors describe the EMM constructs and its three layers (i.e., schema, presentation, and editing) The schema defines the structure of the data, the presentation selects what data to display, and the editing layer uses projections

to encode, visualize, and apply changes to RDF data Particular focus is given to a formal description of the EMM and to the potential implementation of this model in the GUI of a semantic work environment

At the end of the chapter they provide a set of related technologies for modeling semantics for SWEs They think that EMM is useful for developers of data-centric (as opposed to ontology-centric) editors and can serve as a contribution to the ongoing discussion about simpler versions of OWL

Compilation of References 313 About the Contributors 337 Index 346

xiv

Foreword

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

on the WWW

This book, edited by Jörg Rech, Eric Ras, and Björn Decker, deals with another interesting and 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

im-Today, semantic technologies increasingly find their way into collaborative tools such as Wikis, 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

Desk-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

• SweetWiki: A Semantic Wiki that integrates several semantic technologies to provide a Semantic

Web application platform for everyone

xv

• SemperWiki: A Semantic Wiki that is targeted to support personal knowledge management with

semantic technologies

• DeepaMehta: A platform designed to provide knowledge workers with additional information that

supports their work, thoughts, and collaborations with colleagues

• Ylvi: A Semantic Wiki that enables and supports the creation of semantic information during normal

project work

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

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

• How can we integrate people into semantic work environments and show them the added value these systems offer?

• How can we enable and foster learning during work activities and on demand in semantic work environments?

• How can we automatically acquire semantic information from previously existing sources for semantic work environments?

• How can we integrate the various existing technologies for semantic work environments to support project-driven work?

• How can we model the data, metadata, and relations used in semantic work environments?

In summary, the editors have selected a very interesting collection of chapters that present the 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

cur-Prof Dr Klaus-Dieter Althoff

Intelligent Information Systems

University of Hildesheim, Germany

September 2007

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

informa-tion systems He studied mathematics with a focus on expert systems at the University of Technology at Aachen In 1992 he finished his doctoral dissertation on an architecture for knowledge-based technical diagnosis at the University of Kaiserslautern, where he also received the postdoctoral degree (Habilitation) with a thesis on the evaluation of case-based reasoning systems

in 1997 He worked at the Fraunhofer Institute for Experimental Software Engineering as group leader and department head until he went to Hildesheim in April 2004 His main interests include techniques, methods and tools for developing, operating, evaluating, and maintaining knowledge-based systems, with a focus on case-based reasoning, agent technology, experience management, and machine learning.

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 ternet machine-readable (i.e., “Semantic Web in the large”), the SWE approach tackles the problem on

In-a smIn-aller, more focused scIn-ale TIn-ake SemIn-antic Wikis In-as In-an exIn-ample: Wikis In-are enhIn-anced 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

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 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

ap-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 portant information and the way it is presented to the user—anywhere and anytime Approaches such

im-as Wikis are important; however, there is still much work to be done to integrate them into our daily working environments

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

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

using semantic work environments

• Challenge 2: Building semantic work environments to support social collaboration, information

integration, and automated inference

• Challenge 3: Starting semantic work environments and keeping them alive.

• Challenge 4: Adequately presenting information to a user so that it supports the two extremes of

short-term problem solving and long-term competence development

xviii

• 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

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 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

lan-Besides the usage of such technologies, we have to think about how such systems provide tion 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, 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

informa-Book Content

The objective of this book is to provide an overview of the field of semantic work environments by ing 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

bring-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 de-scribes 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

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 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

Opera-in a help-desk application run by FOpera-innish 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 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

Dee-on these cDee-onsideratiDee-ons, they describe their approach of added-value analysis Two applicatiDee-on 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-learn-ing material

Base techniques for building Semantic Work Environments are presented in the final section ter XIII, “Added Automatic Acquisition of Semantics from Text for Semantic Work Environments,”

Chap-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.

Decker, S., Mitra, P., & Melnik, S (2000) Framework for the Semantic Web: An RDF tutorial Internet

Computing, IEEE, 4(6), 68-73.

xxii

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

Völkel, M., Schaffert, S., Pasaru-Bontas, E., & Auer, S (2006) Wiki-based knowledge engineering:

Second workshop on Semantic Wikis Odense, Denmark: ACM Press.

xxiii

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

September 2007

Section I

Introduction



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

content-centered Web 2.0 projects are del.iciou.

us (for Web links), Digg.com (for news), Flickr

(for images), and YouTube (for movies)

Com-munication-centered services such as MySpace

or XING enable individual communication and search for and within spatially distributed com-

munities So-called Web 2.0 mashups integrate

and visualize the collected data and information

in novel ways, unforeseen by the original tent providers The most prominent examples of mashups are based on Google Maps and overlay external content on a map All these developments have a common approach of collecting metadata

con-

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

Provide enhanced means for search and navigation End-user and business

centred Community intelligence Post-encoding of semantics Opaque, homogeneous content

Light-weight S&T

Technology centred Artificial intelligence Pre-encoding of semantics Complex, heterogeneous content

Heavy-weight S&T



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

Point-to-point 1:1 E-mail, SMS/MMS Bidirectional 1:1 IM, VoIP Star-like 1:n Web pages, Blogs, Podcasts Net-like n:m Wikis, content communities



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

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 Content creation within online-communities Science & Technology Conference, publication knowledge bases



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 ViewsList views present a selection of several instances

in a combined view The selection of instances

to display can be either based on class ship (i.e., according to an rdf:type property)

member-or based on the result of a selection by a facet member-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

One building block of the Web 2.0 is the ability of public APIs, callable from embedded Javascripts thus enabling the integration of differ-ent data Several APIs are, for example, available for embedding maps Hence, if instance data in a knowledge base contains property values repre-senting geographical information (i.e., addresses

avail-or longitudes/latitudes) map views can provide information about the geographical location of the selected data (see Figure 1) Depending on the extensibility of the API the integration can be real-ized bidirectional in a way that objects displayed

in the map can be expanded and instance details



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:

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-stance or (b) values of a specific property across

Figure 1 Map view (left) and calendar view (right) of instance data about scientific conferences in OntoWiki



Enabling Social Semantic Collaboration

several instances The latter simplifies the

addi-tion of informaaddi-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 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

program-Concept Identification and Reuse

Knowledge bases become increasingly 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

advanta-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

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/

language identifier. File widget:application uploading of files to the Semantic Web



Enabling Social Semantic Collaboration

also for inexperienced and rare users, already

defined concepts should be suggested to the user

whenever he is requested to contribute new

in-formation In a Web-based environment and for

highly scalable knowledge bases conventional

Web technologies were the major obstacles here,

since they do not support large data sets to be

handled at the client (browser) side The Web 2.0

technologies AJAX and JSON help to overcome

this limitation by enabling to interactively propose

already defined concepts while the user types in

new information to be added to the knowledge

base (see Figure 2)

soCial CollaBoration

aspeCts

A major aim of SSWEs is to foster and employ

social interactions for the development of

knowl-edge bases This eases the structured exchange

of meta-information about the knowledge base

drastically and promotes collaboration

scenar-ios where face-to-face communication is hard

Making means of social interactions as easy as

possible furthermore contributes in creating an

“architecture of participation” that allows users

to add value to the system as they use it In the

following, we elaborate on some examples how

social interactions can be specifically supported

by SSWEs

Change tracking

All changes applied to a knowledge base should

be tracked An SSWE should enable the review

of changes on different levels of detail These

are for example the RDF statement level, the

taxonomy/class hierarchy level, the logic/ontology

level or the domain level The review of changes

should be able to be restricted to a specific

con-text or perspective on the knowledge base, such

as changes on a specific instance, changes on

instances of a certain class, or changes made by

a distinct user or user group In addition to ent such change sets on the Web, users should be able subscribe to get information about the most recent changes on objects of their interest by e-mail or RSS/Atom feeds This again promotes the participation of users

pres-Commenting

Ideally an SSWE allows adding of comments to all information in the knowledge base Commenting (and other tasks in general) can be promoted by reducing the number of clicks and the wait time in between as much as possible This enables com-munity driven discussions, for example about the validity of certain statements Technically, this can

be implemented on the basis of RDF reifications, which allow making statements about statements Small icons attached to an object of a statement within the user interface can indicate that such reifications exist See Figure 3 Positioning the mouse pointer on such an icon will immediately show up a tool tip with the most recent annotations; clicking on the icon will display them all

of rating categories and scales with respect to a

Figure 3 Comments attached to statements.



Enabling Social Semantic Collaboration

certain class Instances of the class can then be

rated according to these categories, thus

allow-ing for example the ratallow-ing of instances of a class

publication according to categories originality,

quality, and presentation

Popularity

All accesses to the knowledge base can be logged,

thus allowing to arrange views on the content

based on popularity As with ratings or user

ac-tivity, the popularity of content can be measured

with respect to a certain knowledge base or

frag-ment of it (e.g., popularity with respect to class

membership) and with respect to a certain time

period (e.g., last hour, day, week, month, etc.)

This enables users to add value to the system as

they use it

Activity/Provenance

The system keeps record of what was contributed

by whom This includes contributions to the

ontol-ogy schema, additions of instance data or ratings,

and commenting This information can be used to

honour active users in the context of the overall

system, a specific knowledge base or a fragment

of it (e.g., instance additions to some class) This

way, it contributes to instantly gratify users for

their efforts and helps building a community

related to certain semantic content

semantiC searCh

To enable users of SSWEs to efficiently search for

information in the knowledge base, the semantic

structuring and representation of content should

be employed to enhance the retrieval of

informa-tion We present two complementary strategies

to achieve this goal

facet-Based Browsing

Taxonomic structures give users exactly one way to access information Furthermore, the development of appropriate taxonomic structures (whether, e.g., class or SKOS keyword hierarchies) requires significant initial efforts As a pay-as-you-go strategy, facet-based browsing allows to reduce the efforts for a priori knowledge structur-ing, while still offering efficient means to retrieve information Thereby, facet-based browsing meth-ods can provide similar functionality for seman-tically rich knowledge bases as tagging systems and tag clouds for Social Software applications Facet-based browsing was first implemented for RDF data by the Longwell Browser.1

To enable users to select objects according

to certain facets, all property values (facets) of

a set of selected instances are analyzed If for a certain property the instances have only a limited set of values, those values are offered to restrict the instance selection further Hence, this way of navigation through data will never lead to empty results The analyzing of property values though can be very resource demanding and time consum-ing To still enable fast response times it can be beneficial to cache the results of of a property value analysis and to selective invalidate cache objects

on updates of respective property values

Semantically Enhanced Full-Text search

OntoWiki provides a full-text search for one or multiple keywords occurring in literal property values Since there can be several property val-ues of a single individual containing the search string the results should be grouped by instances and ordered by frequency of occurrence of the search string Search results may be filtered to contain only individuals which are instances of a distinct class or which are described by the literal only in conjunction with a distinct property (see Figure 4)

0

Enabling Social Semantic Collaboration

A semantic search has significant advantages

compared to conventional full-text searches By

detecting classes and properties, contain matching

instances, the semantic search delivers important

feedback to the user how the search may be

suc-cessfully refined

data integration for sswes

In this section, we give a short overview over

approaches from the Data Integration realm,

which can build a basis for the implementation

of SSWEs

Since the early days of the Web, one of the

most compelling visions has been to develop

new systems that exploit the wealth of available

information to answer new

questions—high-level, semantic queries—that are not directly

answerable using a single document or data item

The Semantic Web represents the most widely

promoted, modern version of this vision, but

actually, as Tim Berners-Lee himself observes

in Berners-Lee (2003), the Semantic Web is a

particular methodology for attempting to achieve

data integration—a topic studied previously in

the AI agents community (Arens, Chee, Hsu, & Knoblock, 1993; Friedman & Weld, 1997) and the data integration field (Chawathe et al., 1994; Levy, Rajaraman, & Ordille, 1996)

Data integration is an incredibly challenging problem: data is both physically encoded and logically represented in many different ways, and

“meaning” or “semantics” is highly dependent on

a frame of reference Over time, many of the cal encoding issues have been largely addressed through standardization; likewise for basic pro-tocols for fetching data Today, most data sources provide access in one of a handful of physical formats: a serialization hidden underneath JDBC drivers, HTML, comma-separated values, RDF, or XML Data is often fetched using HTTP, perhaps layered over with Web Service protocols, as we see with many services from Amazon, Google, and so on This leaves the hardest problems in the

physi-areas of semantics: obtaining semantic

annota-tions for data items; determining a unified output representation for the data when it is combined;

and converting the data from source to output

Each of these areas was originally addressed by asking the content creator or service builder to encode everything; but in recent years there has

Figure 4 User interface for the semantic enhanced search in OntoWiki After searching for “York”

it is suggested to refine the search to instances with one of the properties swrc:address, swrc: booktitle or swrc:name



Enabling Social Semantic Collaboration

been a trend towards incorporating Web 2.0-like

ideas with a human in the loop

semantic annotation

Early work on information integration was divided

into two classes of approaches: integration of

databases focuses on settings where the data was

already stored in relational tables or in objects,

which had a considerable amount of metadata

describing semantics; integration of Web sources

focused on developing wrapper induction

strate-gies (Kushmerick, Weld, & Doorenbos, 1997)

for learning how to extract (and hence annotate)

certain data items from Web pages The Semantic

Web has adopted a strategy that greatly resembles

that of database integration: data items are

anno-tated with a very rich set of metadata, including

class relationships that are based on Knowledge

Representation formalisms

In certain settings, however, we are beginning

to see the emergence of Web 2.0-like ideas, such as

collaborative tagging and instant gratification, in

this space The Mangrove project (Halevy, Etzioni,

Doan, & Ives, 2003) focused on encouraging Web

contributors to semantically annotate their pages

(using a small set of predefined tags) by providing

useful “out of the box” services that exploited these

tags, such as organizational event calendars To

a significant extent, today’s tag-based Web sites

follow a similar model, although to the best of

our knowledge there has not yet been a focus on

integrating tag-based services with query services

that go beyond basic keyword querying

Developing a Unified Output

representation

Most approaches to integrating data are instances

of the so-called mediator architecture

(Wieder-hold, 1992): they build an “umbrella” layer and

representation over disparate data sources A

challenge lies in developing the necessary unified

schema or ontology to represent the combined

knowledge In some cases, this is fairly forward as concepts align closely, but in other cases this can be a major endeavor

straight-In recent years, there has been a focus on weight” integration into commonly understood data representations: overlaid maps, timelines, itemized lists, simple visualizations of one or two attributes Such target representations are limited

“light-in the questions they can answer, but for the right questions, their integrated views are very useful The most common example of this approach is the plethora of map mashups available over Google Maps However, recent research projects like SIMILE (Huynh et al., 2005) exploit very similar ideas using Semantic Web technologies

A complementary approach is to allow laborative authoring of the target knowledgebase One prime example of this is GoogleBase, which has an extensible set of item types and attributes, but which suggests a starting schema for most common categories of items This establishes some regularity across all items of a related type The Open Directory Project (dmoz.org) adopts a slightly different strategy, with a single Web site classification taxonomy that can be edited by any contributor

col-Converting the data

Probably the most challenging aspect of data integration is the process of actually combining the data As described previously, some of the lower levels of this process have been largely alleviated, due to common data encodings and Web Service or HTTP interfaces This leaves most of the emphasis on combining, restructur-ing, and translating attributes and classes from the source representation to the target Traditional data integration achieves this using declarative query languages The Semantic Web approach relies on expressing equivalences or subsump-tion relationships between classes in different knowledge bases; this works for many situations but cannot perform, for instance, mathematical



Enabling Social Semantic Collaboration

transformations such as unit conversion For both

of these cases, a variety of tools have been built to

perform semi-automated schema matching

(Au-mueller, 2005; Doan, Domingos, & Halevy, 2001;

Gal, Modica, & Jamil, 2004; Jian, Hu, Cheng, &

Qu, 2005; Madhavan, Bernstein, & Rahm, 2001;

McGuinness, Rikes, Rice, & Wilder, 2000; Noy

& Musen, 2000); unfortunately, this is a very

challenging problem, and hence such tools are

somewhat prone to mistakes

Web 2.0 generally relies on mashups built

using AJAX—Asynchronous Javascript And

XML—which express the transformations using

custom procedural code over XML data Most

mashups are highly effective, in part because

they focus on simple, lightweight conversions

with well-understood data They are developed

grass-roots-style, by third party programmers who

wanted functionality not available elsewhere

The MOBS system (McCann et al, 05) takes

the schema matching approach and infuses it with

community-based ideas, to great effect MOBS

relies on a large community of users to correct

and refine schema mappings between sources,

and it focuses on providing reward mechanisms

for these users

related work

In addition to the references given in the

preced-ing sections, we would like to exhibit here some

work with regard to extending Social Software

with semantic enhancements in the light of the

earlier mentioned social communication patterns,

that is, point-to-point, bidirectional, star-like,

and net-like

Supplementary to these categories, local

approaches which can be subsumed under the

Semantic Desktop concept play a crucial role

for integrated support of SSWEs The project

SemperWiki (Oren, 2005), for example, created a

simple Wiki, combined with an RDF triple store

for the desktop, which can function as a local basis

for content to be exchanged and distributed in a social, semantic collaboration network Another example is semantically enhanced file systems such as SemDAV (Schandl, 2006) and TagFS (Bloehdorn et al, 2006) The latter extends the file system metaphor with tags, while SemDAV’s aim is a deeper integration of Semantic Web technologies

Social Software founded on the point-to-point

or bidirectional communication patterns is

en-hanced for example in McDowell et al (2004) which provides multiple semantic enhancements for the traditional e-mail service Franz and Staab (2005) show how semantic enhancements can be integrated into instant messaging

The group of star-like semantic social ware is dominated by technologies which enrich textual information in news feeds and traditional Web pages with machine-interpretable semantics (e.g., for RSS (Hammersley, 2003) and ATOM (Nottingham & Sayre, 2005) or GRDDL (Hazặl-Massieux & Connolly, 2005), RDFa (Adida & Birbeck, 2006) and Microformats (Khare & Celik, 2006) for Web pages)

soft-The by far largest group are net-like

approach-es, which can be categorized into (a) semantic enhancements for decentralized technologies like Wikis, Blogs, and P2P networks (e.g., Haase et al., 2004; Kager et al., 2004; Souzis, 2005), (b) Ontologies for the description of content in social networks (e.g., FOAF (Brickley & Miller, 2004) and SIOC (Breslin et al., 2006)), and (c) centralized services for specialized collaboration communi-ties (e.g., flickr.com, del.icio.us and last.fm)

future researCh direCtions

As we mentioned earlier, a pivotal Social Software concept is the collaborative tagging of content leading to folksonomies (i.e., taxonomies created

by folks) (Golder et al., 2006) A folksonomy here

is a system of weighted keywords emerged from

a multiplicity of individually chosen keyword



Enabling Social Semantic Collaboration

tachments (i.e., tags) to content objects Applied

to the sphere of the Semantic Web the challenge

is to employ human “swarm intelligence” in the

spirit of tagging to create not just comprehensive

but also consistent knowledge bases Consistent

here is meant less from a logical point of view than

from the perspective of achieving an agreement

in the user community When knowledge bases

are collaboratively developed by loosely-coupled

communities a way to improve the consistency is

the development of methodologies for moderation

and decision processes within SSWEs A field

of future research are possible indicators for the

degree of consistency

A different approach of tackling the

consis-tency problem represents policies and access

control mechanisms for accessing, editing, and

annotating content Often knowledge bases start

as simple semantic networks, become increasingly

rich thus resulting in taxonomies and class

hierar-chies and are finally enriched with logical axioms

and definitions Due to the variety of the possible

expressivity to be considered, the formulation of

policy models and access control strategies turns

out to be difficult In addition, policies should be

adequate for a spectrum of knowledge bases with

a varying degree of semantic richness

Another challenge, lying less in the scientific

than the software engineering field, is to increase

the flexibility and robustness of storage backends,

libraries, and frameworks for the development of

Semantic Web applications In addition, standards

for semantic widgets and user interface elements

for SSWEs can support user acceptance and

interoperability

Last but not least, economic aspects play a

crucial role to make SSWEs a success Due to the

fact that semantic collaboration is in many cases

not a direct business in itself, specific business

models are needed which are focussed on services

and products supporting the generation and

cura-tion of semantic content by communities

referenCes

Adida, B., & Birbeck, M (2006) RDFa Primer 1.0 (Working Draft) World Wide Web Consor-tium (W3C)

Arens, Y., Chee, C., Hsu, C.-N., & Knoblock,

C (1993) Retrieving and integrating data from

multiple information sources International

Jour-nal on Intelligent and Cooperative Information Systems, 2(2), 127-158.

Auer, S (2005, May 30) Powl – a Web based platform for collaborative Semantic Web develop-

ment In Scripting for the Semantic Web, CEUR

Workshop Proceedings, CEUR-WS (pp 135).

Auer, S., & Herre, H (2006) A versioning and evolution framework for RDF knowledge bases In

Proceedings of Ershov Memorial Conference

Aumüller, D (2005a) SHAWN: Structure helps a

wiki navigate In Proceedings of the

BTW-Work-shop WebDB Meets IR.

Aumüller, D (2005b) Semantic authoring and

retrieval within a wiki (WikSAR) In Demo

Session at the Second European Semantic Web Conference (ESWC2005) Retrieved March 2,

2008, from http://wiksar.sf.netAumueller, D., Do, H H., Massmann, S., & Rahm, E (2005) Schema and ontology match-

ing with COMA++ In SIGMOD Conference (pp

906-908)

Bächle, M (2006) Social Software Informatik

Spektrum, 29(2), 121-124.

Berners-Lee, T (2003, May 20-24) Keynote

ad-dress: Fitting it all together In 12th World Wide

Web Conference, Budapest, Hungary.

Berners-Lee, T., Hendler, J., & Lassila, O (2001,

August) Mein computer versteht mich Spektrum

derWissenschaft, 8, 42-49.



Enabling Social Semantic Collaboration

Bizer, C., Lee, R., & Pietriga, E (2005,

Novem-ber) Fresnel – a browser-independent presentation

vocabulary for RDF In End User Semantic Web

Interaction Workshop at ISWC 2005.

Bloehdorn, S., Görlitz, O., Schenk, S., & Völkel,

M (2006) TagFS - tag semantics for hierarchical

file systems

Brickley, D., & Miller, L (2004) FOAF

vocabu-lary specification FOAF Project

Chawathe, S S., Garcia-Molina, H., Hammer, J.,

Ireland, K., Papakonstantinou, Y., Ullman, J D et

al (1994) The TSIMMIS project: Integration of

heterogeneous information sources IPSJ, 7-18.

Dietzold, S., & Auer, S (2006, June) Access

control on RDFTriple stores from a semanticwiki

perspective In Scripting for the Semantic Web,

CEUR Workshop Proceedings (pp 183) ISSN

1613-0073.

Doan, A., Domingos, P., & Halevy, A (2001)

Reconciling schemas of disparate data sources: A

machine-learning approach In SIGMOD

Confer-ence (pp 509-520).

Franz, T., & Staab, S (2005) SAM: Semantics

aware instant messaging for the networked

se-mantic desktop In Sese-mantic Desktop Workshop

at the ISWC2005, Galway, Ireland

Friedman, M., & Weld, D (1997) Efficiently

executing information-gathering plans IJCAI,

1, 785-791.

Gal, A., Modica, G., & Jamil, H (2004)

Onto-Builder: Fully automatic extraction and

consoli-dation of ontologies from Web sources ICDE,

853

Haase, P., Broekstra, J., Ehrig, M., Menken, M.,

Mika, P., Olko, M et al (2004) Bibster – a

se-mantics-based bibliographic peer-to-peer system

In The Semantic Web – ISWC 2004 (pp 122-136)

Khare, R., & Çelik, T (2006) Microformats: A

pragmatic path to the Semantic Web WWW ‘06

and syntax specification In W3C

Recommenda-tion, WorldWideWeb Consortium (W3C).

Levy, A., Rajaraman, A., & Ordille, J (1996) Querying heterogeneous information sources

using source descriptions VLDB, 251-262.

Madhavan, J., Bernstein, P., & Rahm, E (2001)

Generic schema matching with cupid VLDB,

49-58

McGuinness, D., Fikes, R., Rice, J., & Wilder,

S (2000) The chimaera ontology environment

AAAI/IAAI, 1123-1124.

Nottingham, M., & Sayre, R (2005) The atom

syndication format The Internet Engineering

Task Force (IETF)

Noy, N., & Musen, A (2000) PROMPT: rithm and tool for automated ontology merging

Algo-and alignment AAAI/IAAI, 450-455.



Enabling Social Semantic Collaboration

Oren, E (2005) SemperWiki: A semantic personal

wiki Semantic Desktop Workshop at the ISWC

2005, Galway, Ireland.

Schandl, B (2006) SemDAV: A file exchange

protocol for the semantic desktop CEUR-WS,

202

additional reading

Breslin, J., Decker, S., Harth, A., & Bojars, U

(2006) SIOC: An approach to connect Web-based

communities The International Journal of

Web-Based Communities, 2, 133-142.

Golder, S., & Huberman, B H (2006) Usage

patterns of collaborative tagging systems Journal

of Information Science, 32(2), 198-208.

Karger, D., & Quan, D (2004) What would it

mean to blog on the Semantic Web? In The

Se-mantic Web - ISWC2004 (pp 214-228) Springer

LNCS 3298

Kroetzsch, M., Vrandecic, D., & Völkel, M (2005)

Wikipedia and the Semantic Web - the missing

links Wikimania, Frankfurt, Germany.

Leuf, B., & Cunningham, W (2001) The wiki way

Amsterdam: Addison-Wesley Longman

McCann, R., Kramnik, A., Shen, W., Varadarajan, V., Sobulo, O., & Doan, A (2005) Integrating data from disparate sources: A mass collaboration

approach ICDE, 487-488.

McDowell, L., Etzioni, O., & Halevy, A (2004)

Semantic e-mail: Theory and applications

Jour-nal of Web Semantics, 2, 153-183.

O’Reilly, T (2005) What Is Web 2.0 - design

patterns and business models for the next eration of software Retrieved March 2, 2008,

gen-from http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html

Shirky, C (2003, April) A group is its own worst

enemy In Rede zur ETech Conference Retrieved

March 2, 2008, from ings/group_enemy.html

http://www.shirky.com/writ-Souzis, A (2005) Building a semantic wiki IEEE

Intelligent Systems, 20(5), 87-91.

endnote

1 http://simile.mit.edu/longwell/