A Knowledge Base Approach to Cross-Lingual Keyword Query Interpretation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9981)


The amount of entities in large knowledge bases available on the Web has been increasing rapidly, making it possible to propose new ways of intelligent information access. In addition, there is an impending need for technologies that can enable cross-lingual information access. As a simple and intuitive way of specifying information needs, keyword queries enjoy widespread usage, but suffer from the challenges including ambiguity, incompleteness and cross-linguality. In this paper, we present a knowledge base approach to cross-lingual keyword query interpretation by transforming keyword queries in different languages to their semantic representation, which can facilitate query disambiguation and expansion, and also bridge language barriers. The experimental results show that our approach achieves both high efficiency and effectiveness and considerably outperforms the baselines.


Keyword Queries Knowledge Base Approach Query Interpretation Intelligent Information Access Cross-lingual Lexica 
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.



The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 611346.


  1. 1.
    Pound, J., Mika, P., Zaragoza, H.: Ad-hoc object retrieval in the web of data. In: WWW, pp. 771–780 (2010)Google Scholar
  2. 2.
    Baldwin, T., Lui, M.: Language identification: the long and the short of the matter. In: HLT-NAACL, pp. 229–237 (2010)Google Scholar
  3. 3.
    Zhang, L., Färber, M., Rettinger, A.: XKnowSearch! exploiting knowledge bases for entity-based cross-lingual information retrieval. In: CIKM (2016)Google Scholar
  4. 4.
    Zhang, L., Rettinger, A.: X-LiSA: cross-lingual semantic annotation. PVLDB 7(13), 1693–1696 (2014)Google Scholar
  5. 5.
    Zhang, L., Färber, M., Rettinger, A.: xLiD-Lexica: cross-lingual linked data lexica. In: LREC, pp. 2101–2105 (2014)Google Scholar
  6. 6.
    Zhang, L., Rettinger, A., Thoma, S.: Bridging the gap between cross-lingual NLP and DBpedia by exploiting Wikipedia. In: NLP&DBpedia Workshop (2014)Google Scholar
  7. 7.
    Zhang, L., Rettinger, A.: Exploiting knowledge bases for entity-based multilingual and cross-lingual information retrieval. Technical report.
  8. 8.
    Witten, I., Milne, D.: An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In: WIKIAI, pp. 25–30 (2008)Google Scholar
  9. 9.
    He, H., Wang, H., Yang, J., Yu, P.S.: BLINKS: ranked keyword searches on graphs. In: SIGMOD, pp. 305–316 (2007)Google Scholar
  10. 10.
    Li, G., Ooi, B.C., Feng, J., Wang, J., Zhou, L.: Ease: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data. In: SIGMOD, pp. 903–914(2008)Google Scholar
  11. 11.
    Tran, T., Wang, H., Rudolph, S., Cimiano, P.: Top-k exploration of query candidates for efficient keyword search on graph-shaped (RDF) data. In: ICDE, pp. 405–416 (2009)Google Scholar
  12. 12.
    Kacholia, V., Pandit, S., Chakrabarti, S., Sudarshan, S., Desai, R., Karambelkar, H.: Bidirectional expansion for keyword search on graph databases. In: VLDB, pp. 505–516 (2005)Google Scholar
  13. 13.
    Voorhees, E.M.: Query expansion using lexical-semantic relations. In: SIGIR, pp. 61–69 (1994)Google Scholar
  14. 14.
    Tran, T., Cimiano, P., Rudolph, S., Studer, R.: Ontology-based interpretation of keywords for semantic search. In: Aberer, K., et al. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 523–536. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Demidova, E., Zhou, X., Nejdl, W.: Efficient query construction for large scale data. In: SIGIR, pp. 573–582 (2013)Google Scholar
  16. 16.
    Wei, X., Croft, W.B.: LDA-based document models for ad-hoc retrieval. In: SIGIR, pp. 178–185 (2006)Google Scholar
  17. 17.
    Bendersky, M., Croft, W.B.: Discovering key concepts in verbose queries. In: SIGIR, pp. 491–498(2008)Google Scholar
  18. 18.
    Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In: IJCAI, pp. 1606–1611 (2007)Google Scholar
  19. 19.
    Egozi, O., Markovitch, S., Gabrilovich, E.: Concept-based information retrieval using explicit semantic analysis. ACM Trans. Inf. Syst. 29(2), 8 (2011)CrossRefGoogle Scholar
  20. 20.
    Sorg, P., Cimiano, P.: Cross-language information retrieval with explicit semantic analysis. In: CLEF (Working Notes) (2008)Google Scholar
  21. 21.
    Potthast, M., Stein, B., Anderka, M.: A Wikipedia-based multilingual retrieval model. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 522–530. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute AIFBKarlsruhe Institute of Technology (KIT)KarlsruheGermany

Personalised recommendations