Interactive Query Construction for Keyword Search on the Semantic Web

  • Gideon Zenz
  • Xuan Zhou
  • Enrico Minack
  • Wolf Siberski
  • Wolfgang Nejdl
Part of the Data-Centric Systems and Applications book series (DCSA)


With the growing availability of semantic and structured data on the Web, techniques for intuitive access to these data collections become more important. Therefore, many approaches to keyword search on structured data have been proposed in the recent years. These approaches apply the traditional information retrieval paradigm to structured data, by identifying possible result items in the data collections, scoring them by relevance, and presenting a ranked result list to the user. However, when the user intent is not met by the used scoring algorithm, it is very difficult or impossible for the user to refine the query such that the results reflect the desired intent. To solve this issue, we propose an interactive query construction process. Our system derives possible intentions for the entered keyword query, but instead of presenting results immediately, it guides the user through an interactive process where the user expresses and refines his intention in a few steps until the desired intent is met. In that way, we combine the intuitiveness of keyword search with the expressiveness of semantic queries to satisfy users’ information needs.


Greedy Algorithm Query Evaluation Keyword Query SPARQL Query Triple Pattern 
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.


  1. 1.
    Agrawal, S., Chaudhuri, S., Das, G.: DBXplorer: a system for keyword-based search over relational databases. ICDE (2002). DOI 10.1109/ICDE.2002.994693Google Scholar
  2. 2.
    Bast, H., Weber, I.: The CompleteSearch engine: interactive, efficient, and towards IR& DB integration. CIDR (2007)Google Scholar
  3. 3.
    Bernstein, A., Kaufmann, E.: Gino – a guided input natural language ontology editor. ISWC, pp. 144–157 (2006)Google Scholar
  4. 4.
    Broekstra, J., Kampman, A., van Harmelen, F.: Sesame: a generic architecture for storing and querying RDF and RDF Schema. ISWC (2002)Google Scholar
  5. 5.
    Broughton, V., Heather, L.: Classification schemes revisited: applications to web indexing and searching. J. Inter. Catalog. 2(3/4), 143–155 (2000)Google Scholar
  6. 6.
    Fang, L., Clement, T.Y., Weiyi, M., Abdur, C.: Effective keyword search in relational databases. Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 563–574. Chicago, Illinois, USA (2006)Google Scholar
  7. 7.
    Demidova, E., Zhou, X., Nejdl, W.: Iq\({}^{\mathrm{p}}\): incremental query construction, a probabilistic approach. ICDE 2010, pp. 349–352 (2010)Google Scholar
  8. 8.
    Haller, H.: QuiKey – the smart semantic commandline (a concept). Poster and extended abstract presented at ESWC2008 (2008)Google Scholar
  9. 9.
    Harth, A., Decker, S.: Optimized index structures for querying RDF from the Web. Proceedings of the 3rd Latin American Web Congress (2005)Google Scholar
  10. 10.
    Harth, A., Umbrich, J., Hogan, A., Decker, S.: YARS2: a federated repository for querying graph structured data from the Web. ISWC/ASWC (2007)Google Scholar
  11. 11.
    Jagadish, H.V., Chapman, A., Elkiss, A., Jayapandian, M., Li, Y., Nandi, A., Yu, C.: Making database systems usable. SIGMOD (2007)Google Scholar
  12. 12.
    Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R.E., Thatcher, J.W. (eds.) Complexity of Computer Computations. Plenum Press, NY, USA (1972)Google Scholar
  13. 13.
    Kaufmann, E., Bernstein, A.: How useful are natural language interfaces to the semantic web for casual end-users. ISWC/ASWC, pp. 281–294 (2007)Google Scholar
  14. 14.
    Kaufmann, E., Bernstein, A., Zumstein, R.: Querix: a natural language interface to query ontologies based on clarification dialogs. ISWC, pp. 980–981 (2006)Google Scholar
  15. 15.
    Kimelfeld, B., Sagiv, Y.: Finding and approximating top-k answers in keyword proximity search. PODS (2006). DOI
  16. 16.
    Lei, Y., Uren, V.S., Motta, E.: Semsearch: a search engine for the semantic web. EKAW (2006)Google Scholar
  17. 17.
    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. SIGMOD Conference (2008)Google Scholar
  18. 18.
    Lopez, V., Uren, V., Motta, E., Pasin, M.: Aqualog: an ontology-driven question answering system for organizational semantic intranets. J. Web Semant. 5(2), 72–105 (2007)Google Scholar
  19. 19.
    Minack, E., Sauermann, L., Grimnes, G., Fluit, C., Broekstra, J.: The Sesame LuceneSail: RDF queries with full-text search. Tech. Rep. 2008-1, NEPOMUK (2008)Google Scholar
  20. 20.
    Minack, E., Siberski, W., Nejdl, W.: Benchmarking fulltext search performance of RDF stores. Proceedings of the 6th European Semantic Web Conference (ESWC 2009), pp. 81–95. Heraklion, Greece (2009)Google Scholar
  21. 21.
    Möller, K., Ambrus, O., Josan, L., Handschuh, S.: A visual interface for building SPARQL queries in Konduit. International semantic web conference (posters & demos) (2008)Google Scholar
  22. 22.
    Nandi, A., Jagadish, H.V.: Assisted querying using instant-response interfaces. SIGMOD (2007). DOI
  23. 23.
    Neumann, T., Weikum, G.: RDF-3X: a RISC-style engine for RDF. Proc. VLDB Endowment 1(1), 647–659 (2008)Google Scholar
  24. 24.
    Reichert, M., Linckels, S., Meinel, C., Engel, T.: Student’s perception of a semantic search engine. IADIS CELDA, pp. 139–147. Porto, Portugal (2005)Google Scholar
  25. 25.
    Ruckhaus, E., Vidal, M.E., Ruiz, E.: OnEQL: an ontology efficient query language engine for the semantic web. ALPSWS (2007)Google Scholar
  26. 26.
    Russell, A., Smart, P.R.: NITELIGHT: a graphical editor for SPARQL queries. International semantic web conference (posters & demos) (2008)Google Scholar
  27. 27.
    Selinger, P.G., Astrahan, M.M., Chamberlin, D.D., Lorie, R.A., Price, T.G.: Access path selection in a relational database management system. SIGMOD (1979)Google Scholar
  28. 28.
    Stocker, M., Seaborne, A., Bernstein, A., Kiefer, C., Reynolds, D.: Sparql basic graph pattern optimization using selectivity estimation. Proceedings of the 17th International Conference on World Wide Web, pp. 595–604. Beijing, China (2008)Google Scholar
  29. 29.
    Stojanovic, N., Stojanovic, L.: A logic-based approach for query refinement in ontology-based information retrieval systems. ICTAI 2004. 16th IEEE International Conference on Tools with Artificial Intelligence, pp. 450–457 (2004). DOI 10.1109/ICTAI.2004.13Google Scholar
  30. 30.
    Stuckenschmidt, H., Vdovjak, R., Houben, G.J., Broekstra, J.: Index structures and algorithms for querying distributed RDF repositories. WWW, pp. 631–639 (2004)Google Scholar
  31. 31.
    Tata, S., Lohman, G.M.: SQAK: doing more with keywords. SIGMOD (2008). DOI
  32. 32.
    Tran, T., Cimiano, P., Rudolph, S., Studer, R.: Ontology-based interpretation of keywords for semantic search. ISWC (2007)Google Scholar
  33. 33.
    Wang, H., Zhang, K., Liu, Q., Tran, T., Yu, Y.: Q2semantic: a lightweight keyword interface to semantic search. ESWC, pp. 584–598 (2008)Google Scholar
  34. 34.
    Zenz, G., Zhou, X., Minack, E., Siberski, W., Nejdl, W.: From keywords to semantic queries – incremental query construction on the semantic web. J. Web Semat. 7(3), 166–176 (2009)Google Scholar
  35. 35.
    Zhou, Q., Wang, C., Xiong, M., Wang, H., Yu, Y.: SPARK: adapting keyword query to semantic search. ISWC (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gideon Zenz
    • Xuan Zhou
      • 1
    • Enrico Minack
      • 2
    • Wolf Siberski
      • 3
    • Wolfgang Nejdl
      • 4
    1. 1.L3S Research CenterHannoverGermany
    2. 2.Renmin University of ChinaBeijingChina
    3. 3.Renmin University of ChinaBeijingChina
    4. 4.Renmin University of ChinaBeijingChina

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