Web Searching with Entity Mining at Query Time

  • Pavlos Fafalios
  • Ioannis Kitsos
  • Yannis Marketakis
  • Claudio Baldassarre
  • Michail Salampasis
  • Yannis Tzitzikas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7356)

Abstract

In this paper we present a method to enrich the classical web searching with entity mining that is performed at query time. The results of entity mining (entities grouped in categories) can complement the query answers with useful for the user information which can be further exploited in a faceted search-like interaction scheme. We show that the application of entity mining over the snippets of the top-hits of the answers, can be performed at real-time. However mining over the snippets returns less entities than mining over the full contents of the hits, and for this reason we report comparative results for these two scenarios. In addition, we show how Linked Data can be exploited for specifying the entities of interest and for providing further information about the identified entities, implementing a kind of entity-based integration of documents and (semantic) data. Finally, we discuss the applicability of this approach on professional search, specifically for the domains of fisheries/aquaculture and patents.

Keywords

Query Time Jaccard Similarity SPARQL Query Query Answer Link Open Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Bishop, B., Kiryakov, A., Ognyanov, D., Peikov, I., Tashev, Z., Velkov, R.: Factforge: A fast track to the web of data. Semantic Web 2(2), 157–166 (2011)Google Scholar
  2. 2.
    Bonino, D., Ciaramella, A., Corno, F.: Review of the state-of-the-art in patent information and forthcoming evolutions in intelligent patent informatics. World Patent Information 32(1) (2010)Google Scholar
  3. 3.
    Bontcheva, K., Tablan, V., Maynard, D., Cunningham, H.: Evolving GATE to meet new challenges in language engineering. Nat. Lang. Eng. 10, 349–373 (2004)CrossRefGoogle Scholar
  4. 4.
    Cheng, T., Chang, K.C.C.: Entity search engine: Towards agile best-effort information integration over the web. In: Proc. of CIDR, pp. 108–113. Citeseer (2007)Google Scholar
  5. 5.
    Cheng, T., Yan, X., Chang, K.C.C.: Entityrank: searching entities directly and holistically. In: Procs. of the 33rd Intern. VLDB Conf., pp. 387–398 (2007)Google Scholar
  6. 6.
    Cheng, T., Yan, X., Chang, K.C.C.: Supporting entity search: a large-scale prototype search engine. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 1144–1146. ACM (2007)Google Scholar
  7. 7.
    Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications. In: Procs of the 40th Anniversary Meeting of the Association for Computational Linguistics, ACL 2002 (2002)Google Scholar
  8. 8.
    Ernde, B., Lebel, M., Thiele, C., Hold, A., Naumann, F., Barczyn’ski, W., Brauer, F.: ECIR - a Lightweight Approach for Entity-centric Information Retrieval. In: Proceedings of the 18th Text REtrieval Conference, TREC 2010 (2010)Google Scholar
  9. 9.
    Fafalios, P., Kitsos, I., Tzitzikas, Y.: Scalable, flexible and generic instant overview search. In: WWW 2012 (Demo Paper), Lyon (April 2012)Google Scholar
  10. 10.
    Fafalios, P., Tzitzikas, Y.: Exploiting Available Memory and Disk for Scalable Instant Overview Search. In: Bouguettaya, A., Hauswirth, M., Liu, L. (eds.) WISE 2011. LNCS, vol. 6997, pp. 101–115. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Ferré, S., Hermann, A.: Semantic Search: Reconciling Expressive Querying and Exploratory Search. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 177–192. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Fuhr, N.: An infrastructure for supporting the evaluation of interactive information retrieval. In: Procs of the 2011 Workshop on Data Infrastructures for Supporting Information Retrieval Evaluation, DESIRE 2011, NY, USA (2011)Google Scholar
  13. 13.
    Joho, H., Azzopardi, L., Vanderbauwhede, W.: A survey of patent users: an analysis of tasks, behavior, search functionality and system requirements. In: Procs of the 3rd Symposium on Information Interaction in Context. ACM (2010)Google Scholar
  14. 14.
    Kohn, A., Bry, F., Manta, A.: Professional Search: Requirements, Prototype and Preliminary Experience Report (2008)Google Scholar
  15. 15.
    Manolis, N., Tzitzikas, Y.: Interactive Exploration of Fuzzy RDF Knowledge Bases. In: Procs of the 8th Extended Semantic Web Conference, ECSW 2011 (2011)Google Scholar
  16. 16.
    Papadakos, P., Armenatzoglou, N., Kopidaki, S., Tzitzikas, Y.: On exploiting static and dynamically mined metadata for exploratory web searching. Knowledge and Information Systems 30, 493–525 (2012), doi:10.1007/s10115-011-0388-2CrossRefGoogle Scholar
  17. 17.
    Papadakos, P., Kopidaki, S., Armenatzoglou, N., Tzitzikas, Y.: Exploratory Web Searching with Dynamic Taxonomies and Results Clustering. In: Agosti, M., Borbinha, J., Kapidakis, S., Papatheodorou, C., Tsakonas, G. (eds.) ECDL 2009. LNCS, vol. 5714, pp. 106–118. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  18. 18.
    Sacco, G.M., Tzitzikas, Y.: Dynamic taxonomies and faceted search: theory, practice, and experience, vol. 25. Springer-Verlag New York Inc. (2009)Google Scholar
  19. 19.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Procs. of the 16th World Wide Web Conf., pp. 697–706 (2007)Google Scholar
  20. 20.
    van Zwol, R., Garcia Pueyo, L., Muralidharan, M., Sigurbjörnsson, B.: Machine learned ranking of entity facets. In: Procs. of the 33rd Intern. ACM SIGIR Conf., pp. 879–880. ACM (2010)Google Scholar
  21. 21.
    van Zwol, R., Sigurbjornsson, B., Adapala, R., Garcia Pueyo, L., Katiyar, A., Kurapati, K., Muralidharan, M., Muthu, S., Murdock, V., Ng, P., et al.: Faceted exploration of image search results. In: Procs. of the 19th World Wide Web (2010)Google Scholar
  22. 22.
    Zamir, O., Etzioni, O.: Web document clustering: A feasibility demonstration. In: Procs of SIGIR 1998, Melbourne, Australia, pp. 46–54 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pavlos Fafalios
    • 1
  • Ioannis Kitsos
    • 1
  • Yannis Marketakis
    • 1
  • Claudio Baldassarre
    • 2
  • Michail Salampasis
    • 3
  • Yannis Tzitzikas
    • 1
  1. 1.Institute of Computer Science, FORTH-ICS, and Computer Science DepartmentUniversity of CreteGreece
  2. 2.Food and Agriculture Organization of the United NationsItaly
  3. 3.Institute of Software Technology, and Interactive SystemsVienna Univ. of TechnologyAustria

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