Snippet-Based Relevance Predictions for Federated Web Search

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7814)


How well can the relevance of a page be predicted, purely based on snippets? This would be highly useful in a Federated Web Search setting where caching large amounts of result snippets is more feasible than caching entire pages. The experiments reported in this paper make use of result snippets and pages from a diverse set of actual Web search engines. A linear classifier is trained to predict the snippet-based user estimate of page relevance, but also, to predict the actual page relevance, again based on snippets alone. The presented results confirm the validity of the proposed approach and provide promising insights into future result merging strategies for a Federated Web Search setting.


Federated Web search snippets classification relevance judgments 


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  1. 1.
    Arguello, J., Callan, J., Diaz, F.: Classification-based resource selection. In: CIKM 2009. ACM Press, New York (2009)Google Scholar
  2. 2.
    Clarke, C.L.A., Craswell, N., Soboroff, I., Cormack, G.V.: Overview of the TREC 2010 Web Track. In: TREC, pp. 1–9 (2010)Google Scholar
  3. 3.
    Demeester, T., Nguyen, D., Trieschnigg, D., Develder, C., Hiemstra, D.: What Snippets Say about Pages in Federated Web Search. In: Hou, Y., Nie, J.-Y., Sun, L., Wang, B., Zhang, P. (eds.) AIRS 2012. LNCS, vol. 7675, pp. 250–261. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Nguyen, D., Demeester, T., Trieschnigg, D., Hiemstra, D.: Federated Search in the Wild: the Combined Power of over a Hundred Search Engines. In: CIKM 2012 (2012)Google Scholar
  5. 5.
    Nigam, K., Lafferty, J., Mccallum, A.: Using Maximum Entropy for Text Classification. In: IJCAI 1999 Workshop on Information Filtering (1999)Google Scholar
  6. 6.
    Shokouhi, M., Li, L.: Federated Search. Foundations and Trends in Information Retrieval 5(1), 1–102 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Ghent University - iMindsGhentBelgium
  2. 2.University of TwenteEnschedeThe Netherlands

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