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A reverse engineering approach for automatic annotation of Web pages

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Abstract

The Semantic Web is gaining increasing interest to fulfill the need of sharing, retrieving, and reusing information. Since Web pages are designed to be read by people, not machines, searching and reusing information on the Web is a difficult task without human participation. To this aim adding semantics (i.e meaning) to a Web page would help the machines to understand Web contents and better support the Web search process. One of the latest developments in this field is Google’s Rich Snippets, a service for Web site owners to add semantics to their Web pages. In this paper we provide a structured approach to automatically annotate a Web page with Rich Snippets RDFa tags. Exploiting a data reverse engineering method, combined with several heuristics, and a named entity recognition technique, our method is capable of recognizing and annotating a subset of Rich Snippets’ vocabulary, i.e., all the attributes of its Review concept, and the names of the Person and Organization concepts. We implemented tools and services and evaluated the accuracy of the approach on real E-commerce Web sites.

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Notes

  1. http://linkeddata.org/

  2. For more details see http://www.surl.org/.

  3. http://www.w3.org/TR/xhtml-rdfa-primer/

  4. http://microformats.org/about

  5. http://www.itl.nist.gov/iad/894.02/related_projects/muc/index.html

  6. http://www.daviddlewis.com/resources/testcollections/reuters21578/

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Authors and Affiliations

  1. Dipartimento di Informatica e Automazione, Universitá Roma Tre, Rome, Italy

    Roberto De Virgilio

  2. Erasmus School of Economics, Erasmus University Rotterdam, PO Box 1738, 3000 DR, Rotterdam, The Netherlands

    Flavius Frasincar, Walter Hop & Stephan Lachner

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  1. Roberto De Virgilio
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  2. Flavius Frasincar
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  3. Walter Hop
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  4. Stephan Lachner
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Correspondence to Roberto De Virgilio.

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De Virgilio, R., Frasincar, F., Hop, W. et al. A reverse engineering approach for automatic annotation of Web pages. Multimed Tools Appl 64, 119–140 (2013). https://doi.org/10.1007/s11042-011-0852-8

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