Diversified Top-k Keyword Query Interpretation on Knowledge Graphs

  • Ying Wang
  • Ming ZhongEmail author
  • Yuanyuan Zhu
  • Xuhui Li
  • Tieyun Qian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10366)


Exploring a knowledge graph through keyword queries to discover meaningful patterns has been studied in many scenarios recently. From the perspective of query understanding, it aims to find a number of specific interpretations for ambiguous keyword queries. With the assistance of interpretation, the users can actively reduce the search space and get more relevant results.

In this paper, we propose a novel diversified top-k keyword query interpretation approach on knowledge graphs. Our approach focuses on reducing the redundancy of returned results, namely, enriching the semantics covered by the results. In detail, we (1) formulate a diversified top-k search problem on a schema graph of knowledge graph for keyword query interpretation; (2) define an effective similarity measure to evaluate the semantic similarity between search results; (3) present an efficient search algorithm that guarantees to return the exact top-k results and minimize the calculation of similarity, and (4) propose effective pruning strategies to optimize the search algorithm. The experimental results show that our approach improves the diversity of top-k results significantly from the perspectives of both statistics and human cognition. Furthermore, with very limited loss of result precision, our optimization methods can improve the search efficiency greatly.


Diversification Keyword query interpretation Top-k search Knowledge graph 



This work was supported by National Natural Science Foundation of China under contracts 61202036, 61572376, 61502349, and 61272110, and by Wuhan Morning Light Plan of Youth Science and Technology under contract 2014072704011250.


  1. 1.
    Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: WSDM, pp. 5–14 (2009)Google Scholar
  2. 2.
    Angel, A., Koudas, N.: Efficient diversity-aware search. In: SIGMOD, pp. 781–792 (2011)Google Scholar
  3. 3.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-76298-0_52 CrossRefGoogle Scholar
  4. 4.
    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD, pp. 1247–1250 (2008)Google Scholar
  5. 5.
    Pound, J., IIyas, I.F., Weddell, G.: Expressive and flexible access to web-extracted data: a keyword-based structured query language. In: SIGMOD, pp. 423–434 (2010)Google Scholar
  6. 6.
    Pound, J., Hudek, A.K., IIyas, I.F., Weddell, G.: Interpreting keyword queries over web knowledge bases. In: CIKM, pp. 305–314 (2012)Google Scholar
  7. 7.
    Qin, L., Yu, J.X., Chang, L.: Diversifying top-k results. In: VLDB, pp. 1124–1135 (2012)Google Scholar
  8. 8.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge unifying wordnet and wikipedia. In: WWW, pp. 697–706 (2007)Google Scholar
  9. 9.
    Tran, T., Cimiano, P., Rudolph, S., Studer, R.: Ontology-based interpretation of keywords for semantic search. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 523–536. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-76298-0_38 CrossRefGoogle Scholar
  10. 10.
    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. 405–419 (2009)Google Scholar
  11. 11.
    Wu, W., Li, H., Wang, H., Zhu, K.: Probase: a probabilistic taxonomy for text understanding. In: SIGMOD, pp. 481–492 (2012)Google Scholar
  12. 12.
    Wu, Y., Yang, S., Srivatsa, M., Iyengar, A., Yan, X.: Summarizing answer graphs induced by keyword queries. In: VLDB, pp. 1774–1785 (2013)Google Scholar
  13. 13.
    Zeng, Z., Bao, Z., Le, T.N., Lee, M.L., Ling, W.T.: ExpressQ: identifying keyword context and search target in relational keyword queries. In: CIKM, pp. 31–40 (2014)Google Scholar
  14. 14.
    Zhao, F., Zhang, X., Tung, A.K.H., Chen, G.: BROAD: Diversified keyword search in databases. In: VLDB, pp. 1355–1358 (2011)Google Scholar
  15. 15.
    Zhou, Q., Wang, C., Xiong, M., Wang, H., Yu, Y.: SPARK: adapting keyword query to semantic search. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 694–707. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-76298-0_50 CrossRefGoogle Scholar
  16. 16.
    Garbonell, J.G., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: SIGIR, pp. 335–336 (1998)Google Scholar
  17. 17.
    Demidova, E., Fankhauser, P., Zhou, X., Nejdl, W.: DivQ: diversification for keyword search over structured databases. In: SIGIR, pp. 331–338 (2010)Google Scholar
  18. 18.
    Golenberg, K., Kimelfeld, B., Sagiv, Y.: Keyword proximity search in complex data graphs. In: SIGMOD, pp. 927–940 (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ying Wang
    • 1
  • Ming Zhong
    • 1
    Email author
  • Yuanyuan Zhu
    • 1
  • Xuhui Li
    • 1
  • Tieyun Qian
    • 1
  1. 1.State Key Laboratory of Software EngineeringWuhan UniversityWuhanChina

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