Clustering Query Results to Support Keyword Search on Tree Data

  • Cem Aksoy
  • Ananya Dass
  • Dimitri Theodoratos
  • Xiaoying Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8485)


Keyword search conveniently allows users to search for information on tree data. Several semantics for keyword queries on tree data have been proposed in recent years. Some of these approaches filter the set of candidate results while others rank the candidate result set. In both cases, users might spend a significant amount of time searching for their intended result in a plethora of candidates. To address this problem, we introduce an original approach for clustering keyword search results on tree data at different levels. The clustered output allows the user to focus on a subset of the results while looking for the relevant results. We also provide a ranking of the clusters at different levels to facilitate the selection of the relevant clusters by the user. We present an algorithm that efficiently implements our approach. Our experimental results show that our proposed clusters can be computed efficiently and the clustering methodology is effective in retrieving the relevant results.


Keyword Search Query Result Keyword Query Instance Tree Cluster Methodology 
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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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Cem Aksoy
    • 1
  • Ananya Dass
    • 1
  • Dimitri Theodoratos
    • 1
  • Xiaoying Wu
    • 2
  1. 1.New Jersey Institute of TechnologyNewarkUSA
  2. 2.Wuhan UniversityWuhanChina

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