Download Special Issue: Web 2.0, Semantics, Knowledge and Grid

CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE
Concurrency Computat.: Pract. Exper. 2009; 21:557–559
Published online 17 February 2009 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cpe.1374
Special Issue: Web 2.0,
Semantics, Knowledge and
Grid
With the massive participations of worldwide developers and users as well as enabling collaboration
and social networks, Web 2.0 is significantly influencing the development of the Web, Semantic
Web and Grid by aiming at a powerful and harmonious interconnection environment.
The study of the future Web concerns the exploration of the laws among large-scale and expanding
resources such as the formation of community and effective knowledge sharing, the semantics facilitating various interactions (between human, between machines and between human and machine)
the scalable infrastructure and applications.
This special issue is to reflect the up-to-date progresses of research on the future Web. It contains
eight papers selected mainly from the third International Conference on Semantics, Knowledge and
Grid [1], which emphasizes on promoting cross-area research.
Web mining and community discovery play an important role in supporting intelligent Web
applications. Zhang and Xu [2] introduce a set of Web mining methods based on various available
information on the Web, including Web document contents, hyperlink analysis, user access logs
and semantic analysis. Several key Web clustering algorithms are studied, including a latent linkage
information algorithm for finding relevant pages and a Web page clustering algorithm based on the
defined concept of Web page correlation.
Pierce et al. [3] introduce the application of Web 2.0 in the following scenarios: client-side
JavaScript libraries for building and composing Grid services, integrating server-side portlets with
‘rich client’ AJAX tools and Web services for analyzing a global positioning system’s data,
building and analyzing folksonomies of scientific user communities through social bookmarking, and applying microformats and GeoRSS to problems in scientific metadata description and
delivery.
The future Web and Grid need to combine different models and techniques to provide users
with more accessible services and resources. Dillon et al. [4] discuss three important computing
paradigms including service-oriented computing, Grid computing and Web 2.0. An abstracted
distributed computing model is introduced and a Grid architecture GRIDspace is discussed.
Semantic Web plays an important role in making the description of Web resources more machineunderstandable, which can be used as the basic data model and query model to support heterogeneous
resource management on Semantic Grid. Babik and Hluchy present an architecture to support
efficient resource description, query processing and logic reasoning based on a subset of Semantic
Web language [5]. The system combines a tableau algorithm with a resolution-based reasoning
Copyright © 2009 John Wiley & Sons, Ltd.
558
EDITORIAL
method to support scalable queries on large-scale instances with complex schema for resource
management on Semantic Grid.
Ontology technologies are promising and enabling tools for building an intelligent Web. Ungrangsi et al. propose an ontology retrieval framework for the information sharing on the nextgeneration Web [6]. A similarity ranking method is proposed to evaluate the semantic query on
ontology modeled by XML declarative description language.
Logic reasoning is an important tool to utilize knowledge information. Li et al. propose a casebased reasoning method with adaptive learning rules [7]. Resource Space model and Semantic Link
Network model are used for efficient resource management and reuse. By dynamically updating
the reasoning rules, the reasoning results are improved.
Semantics-rich queries are also important for databases that are to manage heterogeneous data
objects. Li et al. [8] presents a semantic model for multimedia database systems. A semantic
schema description framework called MediaView is provided for users to describe the data model
of a multimedia database with more formal semantics. The MediaView model allows users to
design a dynamic and evolving object-oriented schema for multimedia data objects, upon which a
multimedia recipe database is developed.
The future Web faces many problems from architectural solution to scalable and extensible data
model. Sun et al. propose a system architectural framework with a semantic object model for
massive information and knowledge sharing on the future Web [9]. It incorporates peer-to-peer
(P2P) computing techniques into the system platform where an object-oriented semantic model is
provided for users to describe resources in a more flexible and extensible way. The basic system
architecture and related semantic object model are suggested as the scalable semantic model for
the future Web.
We trust this special issue will make a significant contribution to the development of the future
interconnection environment as described in [10].
Finally, we would like to take this opportunity to thank the chief editor, publisher, authors and
reviewers whose strong support made this special issue possible.
REFERENCES
1. SKG 2007. 3rd International Conference on Semantics, Knowledge and Grid, Xi’an, China. Available at: http://www.
knowledgegrid.net.
2. Zhang Y, Xu G. On Web communities mining and recommendation. Concurrency and Computation: Practice and
Experience 2008; DOI: 10.1002/cpe.1366.
3. Pierce ME, Fox GC, Choi JY, Guo Z, Gao X, Ma Y. Using Web 2.0 for scientific applications and scientific communities.
Concurrency and Computation: Practice and Experience 2008; DOI: 10.1002/cpe.1365.
4. Dillon TS, Wu C, Chang E. An abstract layered model for Web inclusive distributed computing leading to enhancing
GRIDSpace with Web 2.0. Concurrency and Computation: Practice and Experience 2008; DOI: 10.1002/cpe.1367.
5. Babik M, Hluchy L. Optimizing description logic reasoning for the large-scale semantic repositories. Concurrency and
Computation: Practice and Experience 2008; DOI: 10.1002/cpe.1371.
6. Ungrangsi R, Anutariya C, Wuwongse V. SQORE: An ontology retrieval framework for the next generation Web.
Concurrency and Computation: Practice and Experience 2009; DOI: 10.1002/cpe.1385.
7. Li H, Li X, Hu D, Hao T, Liu W, Chen X. Adaptation rule learning for case-based reasoning. Concurrency and
Computation: Practice and Experience 2008; DOI: 10.1002/cpe.1368.
8. Li N, Li Q, Wang L, Sun X. A new semantic model with applications in a multimedia database system. Concurrency
and Computation: Practice and Experience 2008; DOI: 10.1002/cpe.1373.
Copyright q
2009 John Wiley & Sons, Ltd.
Concurrency Computat.: Pract. Exper. 2009; 21:557–559
DOI: 10.1002/cpe
EDITORIAL
559
9. Sun X, Zhuge H, Li Q. A framework for the massive knowledge Web. Concurrency and Computation: Practice and
Experience 2008; DOI: 10.1002/cpe.1372.
10. Zhuge H. The Future Interconnection Environment. IEEE Computer 2005; 38(4):27–33.
HAI ZHUGE
China Knowledge Grid Research Group
Institute of Computing Technology
Chinese Academy of Sciences, 10080 Beijing, China
QING LI
Department of Computer Science
City University of Hong Kong
Hong Kong SAR, China
Copyright q
2009 John Wiley & Sons, Ltd.
Concurrency Computat.: Pract. Exper. 2009; 21:557–559
DOI: 10.1002/cpe