Apr 13, 2012 - Description Logic, Logic Programming,. Ontology ... application problems on the Semantic. Web. First, they perform a systematic analysis on the ...
Guest Editorial
Huajun Chen Zhejiang University, CHINA Philippe Cudré-Mauroux Massachusetts Institute of Technology, USA
Special Issue on Semantic Web Meets Computational Intelligence
T
he Semantic Web (SW) [1] carries out the vision of a global web of data directly consumable and understandable to machines. This vast data space,consisting of openlinked data annotated with possibly expressive ontologies, promises significant opportunities for a variety of new web applications. The current Semantic Web languages and technologies are mainly built on traditional Artificial Intelligence approaches such as Description Logic, Logic Programming, Ontology Reasoning, etc. However, in a highly open, decentralized, dynamic, and vast Web environment, the building and development of a global data space calls for more expressive languages capable of dealing with fuzziness and vagueness in web semantics [2][3], and more efficient computational approaches to reduce the complexity of a number of problems inherent to the Semantic Web such as Web-scale querying and reasoning [4], vast decentralized semantic storage [5], and massive linked data analysis, etc. The Computational Intelligence (CI) communities have accumulated a wealth of adaptive approaches such as fuzzy logic system, evolutionary computation, artificial neural networks that are particularly adept in tackling difficult problems in a highly dynamic and decentralized environment such as the Web. More and more researchers from the Semantic Web community are aware
Digital Object Identifier 10.1109/MCI.2012.2188579 Date of publication: 13 April 2012
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of the importance of introducing these CI methodologies and approaches to study the complexity of such a huge web of data. For example, Fuzzy Logic has inspired the design of variant fuzzy extensions of several Semantic Web languages. Nature-inspired optimization methods such as Genetic Algorithms (GA), Swarm Intelligence (SI), Artificial Immune Systems (AIS) have been witnessed in optimizing query answering and reasoning over such a vast, decentralized data space. This special issue aims at promoting the discussion on current trends in the marriage of the Semantic Web and Computational Intelligence. Our goal is not only to introduce applying CI approaches into variant Semantic Web applications, but also present cuttingedge perspectives and visions to highlight future development. After a rigorous peer-review processes, we have finally selected four articles that cover the different aspects of Computational Intelligence applied in the Semantic Web research and development. In the opening article, Christophe Gu´eret et al., describe how evolutionary and swarm computing can be applied to address the most typical application problems on the Semantic Web. First, they perform a systematic analysis on the most typical inference tasks including query, storage, entailment checking, consistency and satisfiability checking, and mapping. They then argue that the existing approaches to address underlying inference tasks necessarily fail given the vastness,
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | MAY 2012
dynamicity and complexity of the data generated by the Semantic Web. For each of these tasks they discuss possible problem-solving methods grounded in evolutionary and swarm computing. Finally, they highlight two successful case studies; the eRDF framework that provides evolutionary algorithms for querying, and a swarm algorithm for logical entailment computation. In the second article, Hannes M¨uhleisen et al., give a positive answer to the question of whether swarm-based approaches are useful in creating largescale distributed storage and reasoning system for the Semantic Web. They present a scalable, adaptive and robust semantic storage system, inspired by Swarm Intelligence, to store and analyze the massive amounts of semantic data. They also emphasize the problem of web-scale reasoning and propose the idea of reasoning over a fully decentralized and self-organized storage system that is based on the collective behavior of swarm individuals. Experiments of storage and reasoning performance are illustrated as well to show the general feasibility and scalability of proposed approaches. In the third article, Jeff Z. Pan et al., study the problem of fuzzy representation and reasoning in the Semantic Web. This article presents how to make use of tractable profiles in OWL 2 and some of their fuzzy extension to provide tractable reasoning services for ontology applications. They describe a reusable reasoning infrastructure called TrOWL, and show how it can be used
to support several use cases such as mashups, process refinements validation, software engineering guidance for tractable applications of fuzzy and crisp ontologies. In the the fourth paper, Liu et al., investigate how cloud infrastructure can help solve scalability issue of fuzzy reasoning in OWL. It reports a MapReduce-based framework that enables scalable reasoning on top of semantic data under fuzzy pD* semantics (i.e., an extension of OWL pD* semantics with fuzzy vagueness). This type of research is important as the content in the Semantic Web is akin to be fuzzy and the size of fuzzy information in it is extraordinary huge, leading to a number of challenges dealing with both the hugeness and vagueness for the Semantic Web content. In summary, the first two articles focus on the applications of variant evolutionary computation approaches in reducing the complexity of a number of
computational problems inherent to the Semantic Web such as query processing, semantic storage, and web-scale reasoning. The next two articles move to the use of fuzzy logic to represent and reason over fuzzy information in the Semantic Web with particular focus on the scalability issue of dealing with vast, fuzzy data in a tractable way. We hope the four selected papers can cover the most essential aspects in the application of CI approaches in the Semantic Web. These aspects range from the state of the art technologies and solutions to tackle the critical challenges faced by the building and development of the Semantic Web, to typical case studies and visions for future development. Moreover, we hope this special issue can shed light on the importance of Computational Intelligence research in the Semantic Web, and stimulate further research in relevant fields. Lastly, we would like to express our appreciation to our distinguished review-
ers whose expertise and professionalism has certainly contributed significantly to the high quality of the special issue. We would also like to thank Dr. Kay Chen Tan, the Editor-In-Chief, for his helpful guidance and contructive suggestions in the whole process of organizing this special issue. Finally, we would like to thank the authors of the selected papers for their innovative research results. References [1] T. Berners-Lee, J. Hendler, and O. Lassila, “The semantic web,” Sci. Amer. Mag., 2001. [2] G. Stoilosa, G. Stamoub, and J. Z. Pan, “Fuzzy extensions of OWL: Logical properties and reduction to fuzzy description logics,” Int. J. Approx. Reason., vol. 51, no. 6, pp. 656–679, July 2010. [3] T. Lukasiewicz and U. Straccia, “Managing uncertainty and vagueness in description logics for the Semantic Web,” J. Web Semantics, vol. 6, pp. 291–308, 2008. [4] D. Fensel and F. van Harmelen, “Unifying reasoning and search to web scale,” IEEE Internet Comput., vol. 11, no. 2, pp. 96–95, 2007. [5] H. Mühleisen, A. Augustin, T. Walther, M. Harasic, K. Teymourian, and R. Tolksdorf, “A self-organized semantic storage service,” in Proc. 12th Int. Conf. Information Integration and Web-Based Applications and Services, Paris, France, Nov. 2010, pp. 357–364.
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