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Improving Text Search on Hybrid Data

  • Huaijie Zhu
  • Xiaochun Yang
  • Bin Wang
  • Yue Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7419)

Abstract

In our real life, there is much hybrid data which contains not only unstructured data but also structured data. In general, the majority techniques of text search on hybrid data are only focused on unstructured data (text) ignoring the structured data. So this may lead a bad ranking of the searching results. In this paper, we describe a new method about improving text search using structured data. Our contributions are summarized as follows: (i) We build the uniform problem model; (ii) Ours is the first approach adopting the mutual information of feature words to qualify the relevance (similarity) between two texts; and (iii) We utilize several rules to consider the structured data to improve text search and build our approach. Finally, experimental results show the relevance function and our approach guarantees the search results with high recall, top-k precision, Mean Average Precision and good search performance, respectively.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Huaijie Zhu
    • 1
  • Xiaochun Yang
    • 1
  • Bin Wang
    • 1
  • Yue Wang
    • 1
  1. 1.College of Information Science and EngineeringNortheastern UniversityLiaoningChina

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