Semantically Enhanced Entity Ranking

  • Gianluca Demartini
  • Claudiu S. Firan
  • Tereza Iofciu
  • Wolfgang Nejdl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5175)

Abstract

Users often want to find entities instead of just documents, i.e., finding documents entirely about specific real-world entities rather than general documents where the entities are merely mentioned. Searching for entities on Web scale repositories is still an open challenge as the effectiveness of ranking is usually not satisfactory. Semantics can be used in this context to improve the results leveraging on entity-driven ontologies. In this paper we propose three categories of algorithms for query adaptation, using (1) semantic information, (2) NLP techniques, and (3) link structure, to rank entities in Wikipedia. Our approaches focus on constructing queries using not only keywords but also additional syntactic information, while semantically relaxing the query relying on a highly accurate ontology. The results show that our approaches perform effectively, and that the combination of simple NLP, Link Analysis and semantic techniques improves the retrieval performance of entity search.

References

  1. 1.
    Allan, J., Raghavan, H.: Using part-of-speech patterns to reduce query ambiguity. In: Proceedings of the 25th ACM SIGIR Conference (2002)Google Scholar
  2. 2.
    Anick, P.G., Tipirneni, S.: The paraphrase search assistant: Terminological feedback for iterative information seeking. In: Proceedings of the 22nd ACM SIGIR Conference (1999)Google Scholar
  3. 3.
    Baeza-Yates, R., Neto, R.: Modern Information Retrieval. ACM Press, New York (1999)Google Scholar
  4. 4.
    Bast, H., Chitea, A., Suchanek, F., Weber, I.: Ester: efficient search on text, entities, and relations. In: Proceedings of the 30th ACM SIGIR Conference (2007)Google Scholar
  5. 5.
    Cheng, T., Yan, X., Chang, K.C.-C.: Entityrank: Searching entities directly and holistically. In: Proceedings of the 23rd International Conference on Very Large Data Bases (2007)Google Scholar
  6. 6.
    Chernov, S., Iofciu, T., Nejdl, W., Zhou, X.: Extracting semantics relationships between wikipedia categories. In: First Workshop on Semantic Wikis, Proceedings, co-located with the ESWC 2006 (2006)Google Scholar
  7. 7.
    Chirita, P.-A., Firan, C.S., Nejdl, W.: Personalized query expansion for the web. In: Proceedings of the 30th ACM SIGIR Conference (2007)Google Scholar
  8. 8.
    Fu, L., Wang, H., Zhu, H., Zhang, H., Wang, Y., Yu, Y.: Making more wikipedians: Facilitating semantics reuse for wikipedia authoring. In: Proceedings of the 6th International Semantic Web Conference (2007)Google Scholar
  9. 9.
    Kasneci, G., Suchanek, F.M., Ifrim, G., Ramanath, M., Weikum, G.: NAGA: Searching and Ranking Knowledge. In: Proceedings of the 24th International Conference on Data Engineering (2008)Google Scholar
  10. 10.
    Kasneci, G., Suchanek, F.M., Ramanath, M., Weikum, G.: How naga uncoils: searching with entities and relations. In: Proceedings of the 16th International World Wide Web Conference (2007)Google Scholar
  11. 11.
    Pasca, M.: Weakly-supervised discovery of named entities using web search queries. In: Proceedings of the 16th Conference on Information and Knowledge Management (2007)Google Scholar
  12. 12.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International World Wide Web Conference (2007)Google Scholar
  13. 13.
    von Brzeski, V., Irmak, U., Kraft, R.: Leveraging context in user-centric entity detection systems. In: Proceedings of the 16th Conference on Information and Knowledge Management (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Gianluca Demartini
    • 1
  • Claudiu S. Firan
    • 1
  • Tereza Iofciu
    • 1
  • Wolfgang Nejdl
    • 1
  1. 1.L3S Research CenterLeibniz Universität HannoverHannoverGermany

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