Semantic URL Analytics to Support Efficient Annotation of Large Scale Web Archives

  • Tarcisio Souza
  • Elena Demidova
  • Thomas Risse
  • Helge Holzmann
  • Gerhard Gossen
  • Julian Szymanski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9398)

Abstract

Long-term Web archives comprise Web documents gathered over longer time periods and can easily reach hundreds of terabytes in size. Semantic annotations such as named entities can facilitate intelligent access to the Web archive data. However, the annotation of the entire archive content on this scale is often infeasible. The most efficient way to access the documents within Web archives is provided through their URLs, which are typically stored in dedicated index files. The URLs of the archived Web documents can contain semantic information and can offer an efficient way to obtain initial semantic annotations for the archived documents. In this paper, we analyse the applicability of semantic analysis techniques such as named entity extraction to the URLs in a Web archive. We evaluate the precision of the named entity extraction from the URLs in the Popular German Web dataset and analyse the proportion of the archived URLs from 1,444 popular domains in the time interval from 2000 to 2012 to which these techniques are applicable. Our results demonstrate that named entity recognition can be successfully applied to a large number of URLs in our Web archive and provide a good starting point to efficiently annotate large scale collections of Web documents.

Notes

Acknowledgments

This work was partially funded by the European Research Council under ALEXANDRIA (ERC 339233) and the COST Action IC1302 (KEYSTONE). Tarcisio Souza is sponsored by a scholarship from CNPq, a Brazilian government institution for scientific development.

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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Tarcisio Souza
    • 1
  • Elena Demidova
    • 1
  • Thomas Risse
    • 1
  • Helge Holzmann
    • 1
  • Gerhard Gossen
    • 1
  • Julian Szymanski
    • 2
  1. 1.L3S Research Center and Leibniz Universität HannoverHannoverGermany
  2. 2.Gdansk University of TechnologyGdańskPoland

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