IJSWIS Special Issue on Linked Data (with free access to lead article by Bizer/Heath/Berners-Lee)

The contents of the latest issue of IJSWIS-- Special issue on Linked Data (with free access to Linked Data- the story so far)

International Journal on Semantic Web & Information Systems (IJSWIS), Vol. 5, Issue 3

Editor-in-Chief: Amit Sheth, Kno.e.sis Center, Wright State University, USA

SPECIAL ISSUE ON: LINKED DATA

Guest Editors: Chris Bizer, Tom Heath, Martin Hepp

1. Preface [Heath, Hepp, Bizer]   Free Access to this article
   
   
2. Linked Data - The Story So Far   Free Access to this article
   
   # Pages: 1-22
   Authors: Bizer, Christian; Heath, Tom; Berners-Lee, Tim
   Affiliations: Freie Universität Berlin, Germany; Talis Information Ltd, UK; Massachusetts Institute of Technology, USA
   The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web.
   These best practices have been adopted by an increasing number of data providers over the last three years, leading
   to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors
   present the concept and technical principles of Linked Data, and situate these within the broader context of related
   technological developments. They describe progress to date in publishing Linked Data on the Web, review applications
   that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.
3. Community-Driven Linked Data Authoring and Production of Consolidated Linked Data
   # Pages: 23-48
   Authors: Shakya, Aman; Takeda, Hideaki; Wuwongse, Vilas
   Affiliations: National Institute of Informatics, Japan; National Institute of Informatics and the University of Tokyo, Japan; Asian Institute of Technology, Thailand
   User-generated content can help the growth of linked data. However, we lack interfaces enabling ordinary people to author linked data. Secondly,
   people have multiple perspectives on the same concept and different contexts. Thirdly, not enough ontologies exist to model various data. Therefore,
   we propose an approach to enable people to share various data through an easy-to-use social platform. Users define their own concepts and multiple
   conceptualizations are allowed. These are consolidated using semi-automatic schema alignment techniques supported by the community. Further,
   concepts are grouped semi-automatically by similarity. As a result of consolidation and grouping, informal lightweight ontologies emerge gradually.
   We have implemented social software, called StYLiD, to realize our approach. It can serve as a platform motivating people to bookmark and share
   different things. It may also drive vertical portals for specific communities with integrated data from multiple sources. Experimental observations
   support the validity of our approach.
4. Searching Linked Objects with Falcons: Approach, Implementation and Evaluation
   # Pages: 49-70
   Authors: Cheng, Gong; Qu,Yuzhong
   Affiliations: Southeast University, China; Southeast University, China
   Along with the rapid growth of the data Web, searching linked objects for information needs and for reusing become emergent for ordinary Web users
   and developers, respectively. To meet the challenge, we present Falcons Object Search, a keyword-based search engine for linked objects. To serve
   various keyword queries, for each object the system constructs a comprehensive virtual document including not only associated literals but also the
   textual descriptions of associated links and linked objects. The resulting objects are ranked by considering both their relevance to the query and their
   popularity. For each resulting object, a query-relevant structured snippet is provided to show the associated literals and linked objects matched with
   the query. Besides, Web-scale class-inclusion reasoning is performed to discover implicit typing information, and users could navigate class hierarchies
   for incremental class-based results filtering. The results of a task-based experiment show the promising features of the system.
5. A URI is Worth a Thousand Tags: From Tagging to Linked Data with MOAT
   # Pages: 71-94
   Authors: Passant, Alexandre; Laublet, Philippe; Breslin, John G.; Decker, Stefan
   Affiliations: National University of Ireland, Ireland; Université Paris-Sorbonne, France; National University of Ireland, Ireland; National University of Ireland, Ireland
   Although tagging is a widely accepted practice on the Social Web, it raises various issues like tags ambiguity and heterogeneity, as well as the lack of
   organization between tags. We believe that Semantic Web technologies can help solve many of these issues, especially considering the use of formal
   resources from the Web of Data in support of existing tagging systems and practices. In this article, we present the MOAT—Meaning Of A Tag—ontology
   and framework, which aims to achieve this goal. We will detail some motivations and benefits of the approach, both in an Enterprise 2.0 ecosystem and
   on the Web. As we will detail, our proposal is twofold: It helps solve the problems mentioned previously, and weaves user-generated content into the
   Web of Data, making it more efficiently interoperable and retrievable.

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For Background: CFP for the special issue on Linked Data

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Previous Special Issue: Scalability and Performance of Semantic Web Systems

Next special issue CFP: New Avenue in Search (Deadline Dec 15, 2009)