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Handbook of Semantic Web Technologies pp 659–700Cite as

Semantic Web Search Engines

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Abstract

The last couple of years have seen an increasing growth in the amount of Semantic Web data made available, and exploitable, on the Web. Compared to the Web, one unique feature of the Semantic Web is its friendly interface with software programs. In order to better serve human users with software programs, supporting infrastructures for finding and selecting the distributed online Semantic Web data are needed. A number of Semantic Web search engines have emerged recently. These systems are based on different design principles and provide different levels of support for users and/or applications. In this chapter, a survey of these Semantic Web search engines is presented, together with the detailed description of the design of two prominent systems: Swoogle and Watson. The way these systems are used to enable domain applications and support cutting-edge research on Semantic Web technologies is also discussed. In particular, this chapter includes examples of a new generation of semantic applications that, thanks to Semantic Web search engines, exploit online knowledge at runtime, without the need for laborious acquisition in specific domains. In addition, through collecting large amounts of semantic content online, Semantic Web search engines such as Watson and Swoogle allow researchers to better understand how knowledge is formally published online and how Semantic Web technologies are used. In other terms, by mining the collected semantic documents, it becomes possible to get an overview and explore the Semantic Web landscape today.

The first section below (Sect. 16.1) presents a general overview of the area, including the main challenges, related systems, as well as an abstract specification of what is called Semantic Web search engines. It also includes a detailed overview of the two systems more specifically considered as case studies, Swoogle (Sect. 16.1.4) and Watson (Sect. 16.1.5). Section 16.2 shows how these systems are currently being used and applied, both as development platforms to make possible the realization of applications exploiting Semantic Web content (Sect. 16.2.1), and as research platforms, allowing one to better understand the content of the Semantic Web, how knowledge is published online and how it is structured. Finally, Sect. 16.3 briefly introduces other resources to be considered in the area of Semantic Web search engines, and Sect. 16.4 concludes the chapter.

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Authors and Affiliations

  1. Knowledge Media Institute, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK

    Dr. Mathieu d’Aquin & Enrico Motta (Research Scientist, Tetherless World Constellation)

  2. Tetherless World Constellation, Computer Science Department, Rensselaer Polytechnic Institute, 110 8th St., Troy, NY, 12180, USA

    Li Ding

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  1. Dr. Mathieu d’Aquin
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  2. Li Ding
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  3. Enrico Motta
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Correspondence to Mathieu d’Aquin .

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Editors and Affiliations

  1. Knowledge Media Institute, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK

    John Domingue

  2. STI Innsbruck, University of Innsbruck, Technikerstraße 21a, 6020, Innsbruck, Austria

    Dieter Fensel

  3. Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    James A. Hendler

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d’Aquin, M., Ding, L., Motta, E. (2011). Semantic Web Search Engines. In: Domingue, J., Fensel, D., Hendler, J.A. (eds) Handbook of Semantic Web Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92913-0_16

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