IJSWIS 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
ISSN: 1552-6283 EISSN: 1552-6291
http://www.igi-global.com/ijswis

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

Thomson-Scientific Impact factor of this journal: 1.8

GUEST EDITORIAL PREFACE
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.



PAPER ONE

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.
http://infosci-on-demand.com/content/details.asp?ID=33737

PAPER TWO

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.
http://infosci-on-demand.com/content/details.asp?ID=33738

PAPER THREE

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.
http://infosci-on-demand.com/content/details.asp?ID=33739

PAPER FOUR

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.
http://infosci-on-demand.com/content/details.asp?ID=33740

====

Next issue: Linked Data

with State of the Art review paper
"Linked Data - The Story So Far"

by

Chris Bizer, Tom Heath and Tim Berners-Lee

===

Authors interested in submitting mansucript:

http://ijswis.org