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IJSWIS Special Issue on Scalability and Performance of Semantic Web Systems

From: Martin Hepp (UniBW) <martin.hepp@ebusiness-unibw.org>
Date: Wed, 15 Jul 2009 09:54:55 +0200
Message-ID: <4A5D8B4F.4060009@ebusiness-unibw.org>
To: semantic-web at W3C <semantic-web@w3c.org>
(apologies for cross-posting)
Dear all:

We are pleased to announce the latest issue of IJSWIS, which is a  
special issue on "Scalability and Performance of Semantic Web Systems"


The contents of the latest issue of:

International Journal on Semantic Web and Information Systems (IJSWIS)
Volume 5, Issue 2, April-July 2009
Published: Quarterly in Print and Electronically
ISSN: 1552-6283 EISSN: 1552-6291

Editor-in-Chief: Amit Sheth, Kno.e.sis Center, Wright State  University, USA
Associate Editors: 
Martin Hepp, Bundeswehr University Munich, Germany
Gottfried Vossen, University of Muenster, Germany

Impact factor of this journal: 1.8


JSWIS 5(2)

Vassilis Christophides, Institute of Computer Science Foundation for  
Research, Greece
Jeff Heflin, Lehigh University, USA

Recently, the W3C Linking Open Data effort has boosted the publication  
and interlinkage of larger amounts of RDF/S datasets on the Semantic  
Web (SW). Various ontologies and knowledge bases with millions of RDF/ 
S triplets from Wikipedia and other sources have been created and are  
available online. It is clear that the increasing number and size of  
the available SW datasets presents a real challenge for Semantic Web  
systems in order to cope with scalability and performance concerns. In  
this special issue, four articles cover a wide range of techniques for  
benchmarking or enhancing the scalability of Semantic Web systems. The  
authors build systems that process terabytes of data, have response  
times on the order of seconds or less, and rely on reasoning to solve  
problems not easily solved before.

To read the guest editorial preface, please consult this issue of  
IJSWIS in your library.


The Berlin SPARQL Benchmark

Christian Bizer, Freie Universität Berlin, Germany
Andreas Schultz, Freie Universität Berlin, Germany

The SPARQL Query Language for RDF and the SPARQL Protocol for RDF are  
implemented by a growing number of storage systems and used within  
enterprise and open Web settings. As SPARQL is taken by the community,  
there is a growing need for benchmarks to compare the performance of  
storage systems that expose SPARQL endpoints via the SPARQL protocol.  
Such systems include native RDF stores as well as systems that rewrite  
SPARQL queries to SQL queries against non-RDF relational databases.  
This article introduces the Berlin SPARQL Benchmark (BSBM) for  
comparing the performance of native RDF stores with the performance of  
SPARQL-to-SQL rewriters across architectures. This article discusses  
the design of the BSBM benchmark and presents the results of a  
benchmark experiment comparing the performance of four popular RDF  
stores with the performance of two SPARQL-to-SQL rewriters as well as  
the performance of two relational database management systems.

To obtain a copy of the entire article, click on the link below.


Learning of OWL Class Descriptions on Very Large Knowledge Bases

Sebastian Hellmann, Universität Leipzig, Germany
Jens Lehmann, Universität Leipzig, Germany
Sören Auer, Universität Leipzig, Germany

The vision of the Semantic Web is to make use of semantic  
representations on the largest possible scale - the Web. Large  
knowledge bases such as DBpedia, OpenCyc, GovTrack, and others are  
emerging and are freely available as linked data and SPARQL endpoints.  
Exploring and analysing such knowledge bases is a significant hurdle  
for Semantic Web research and practice. As one possible direction for  
tackling this problem, the authors present an approach for obtaining  
complex class descriptions from objects in knowledge bases by using  
machine learning techniques. They describe in detail how to leverage  
existing techniques to achieve scalability on large knowledge bases  
available as SPARQL endpoints or linked data.

To obtain a copy of the entire article, click on the link below.


Scalable Authoritative OWL Reasoning for the Web

Aidan Hogan, National University of Ireland, Ireland
Andreas Harth, National University of Ireland, Ireland
Axel Polleres, National University of Ireland, Ireland

In this article, the authors discuss the challenges of performing  
reasoning on large scale RDF datasets from the Web. Using ter-Horst’s  
pD* fragment of OWL as a base, the authors compose a rule-based  
framework for application to Web data; they argue their decisions  
using observations of undesirable examples taken directly from the  
Web. The authors further temper their OWL fragment through  
consideration of “authoritative stheirces,” which counter-acts an  
observed behavitheir which we term “ontology hijacking”. This article  
presents a system for performing rule-based forward-chaining reasoning  
which they call SAOR (scalable authoritative OWL reasoned). Based upon  
observed characteristics of Web data and reasoning in general, the  
authors design their system to scale. The authors evaluate their  
methods on a dataset in the order of a hundred million statements  
collected from real-world Web stheirces and present scale-up  
experiments on a dataset in the order of a billion statements  
collected from the Web.

To obtain a copy of the entire article, click on the link below.


Enabling Scalable Semantic Reasoning for Mobile Services

Luke Albert Steller, Monash University, Australia
Shonali Krishnaswamy, Monash University, Australia
Mohamed Methat Gaber, Monash University, Australia

With the emergence of high-end smart phones/PDAs there is a growing  
opportunity to enrich mobile/pervasive services with semantic  
reasoning. This article presents novel strategies for optimising  
semantic reasoning for realizing semantic applications and services on  
mobile devices. The authors develop the mTableaux algorithm, which  
optimizes the reasoning process to facilitate service selection. This  
article presents comparative experimental results on the performance  
and scalability of semantic reasoning for mobile devices.

To obtain a copy of the entire article, click on the link below.

For full copies of the above articles, check for this issue of the  
International Journal on Semantic Web and Information Systems (IJSWIS)  
your institution's library.  This journal is also included in the IGI  
Global aggregated "InfoSci-Journals" database: www.infosci-journals.com.


martin hepp
e-business & web science research group
universitaet der bundeswehr muenchen

e-mail:  mhepp@computer.org
phone:   +49-(0)89-6004-4217
fax:     +49-(0)89-6004-4620
www:     http://www.unibw.de/ebusiness/ (group)
         http://www.heppnetz.de/ (personal)
skype:   mfhepp 
twitter: mfhepp

Check out the GoodRelations vocabulary for E-Commerce on the Web of Data!


Talk at the Semantic Technology Conference 2009: 
"Semantic Web-based E-Commerce: The GoodRelations Ontology"

Tool for registering your business:

Overview article on Semantic Universe:

Project page and resources for developers:

Tutorial materials:
Tutorial at ESWC 2009: The Web of Data for E-Commerce in One Day: A Hands-on Introduction to the GoodRelations Ontology, RDFa, and Yahoo! SearchMonkey


Received on Wednesday, 15 July 2009 07:55:43 UTC

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