Making Use of Linked Data for Generating Enhanced Snippets

  • Mazen Alsarem
  • Pierre-Édouard PortierEmail author
  • Sylvie Calabretto
  • Harald Kosch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8798)


We enhance an existing search engine’s snippet (i.e. excerpt from a web page determined at query-time in order to efficiently express how the web page may be relevant to the query) with linked data (LD) in order to highlight non trivial relationships between the information need of the user and LD resources related to the result page. To do this, we introduce a multi-step unsupervised co-clustering algorithm so as to use the textual data associated with the resources for discovering additional relationships. Next, we use a 3-way tensor to mix these new relationships with the ones available from the LD graph. Then, we apply a first PARAFAC tensor decomposition [5] in order to (i) select the most promising nodes for a 1-hop extension, and (ii) build the enhanced snippet. A video demonstration is available online (


Linked data Information retrieval Snippets Co-Clustering Tensor decomposition 


  1. 1.
    Bai, X., Delbru, R., Tummarello, G.: RDF snippets for semantic web search engines. In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part II. LNCS, vol. 5332, pp. 1304–1318. Springer, Heidelberg (2008)Google Scholar
  2. 2.
    Franz, T., Schultz, A., Sizov, S., Staab, S.: TripleRank: ranking semantic web data by tensor decomposition. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 213–228. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Ge, W., Cheng, G., Li, H., Qu, Y.: Incorporating compactness to generate term-association view snippets for ontology search. Inf. Process. Manage. 49(2), 513–528 (2012)CrossRefGoogle Scholar
  4. 4.
    Haas, K., Mika, P., Tarjan, P., Blanco, R.: Enhanced results for web search. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 725–734. ACM (2011)Google Scholar
  5. 5.
    Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: Dbpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems, I-Semantics ’11, pp. 1–8. ACM (2011)Google Scholar
  7. 7.
    Park, L.A.F., Leckie, C.A., Ramamohanarao, K., Bezdek, J.C.: Adapting spectral co-clustering to documents and terms using latent semantic analysis. In: Nicholson, A., Li, X. (eds.) AI 2009. LNCS, vol. 5866, pp. 301–311. Springer, Heidelberg (2009)Google Scholar
  8. 8.
    Pelleg, D., Moore, A.W., et al.: X-means: extending k-means with efficient estimation of the number of clusters. In: ICML, pp. 727–734 (2000)Google Scholar
  9. 9.
    Penin, T., Wang, H., Tran, T., Yu, Y.: Snippet generation for semantic web search engines. In: Domingue, J., Anutariya, C. (eds.) ASWC 2008. LNCS, vol. 5367, pp. 493–507. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Steiner, T., Troncy, R., Hausenblas, M.: How google is using linked data today and vision for tomorrow. In: Proceedings of Linked Data in the Future Internet 700 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mazen Alsarem
    • 1
  • Pierre-Édouard Portier
    • 1
    Email author
  • Sylvie Calabretto
    • 1
  • Harald Kosch
    • 2
  1. 1.Université de Lyon, CNRS, INSA-Lyon, LIRIS, UMR5205LyonFrance
  2. 2.Universität PassauPassauGermany

Personalised recommendations