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CLASCN: Candidate Network Selection for Efficient Top-k Keyword Queries over Databases

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Abstract

Keyword Search Over Relational Databases (KSORD) enables casual or Web users easily access databases through free-form keyword queries. Improving the performance of KSORD systems is a critical issue in this area. In this paper, a new approach CLASCN (Classification, Learning And Selection of Candidate Network) is developed to efficiently perform top-k keyword queries in schema-graph-based online KSORD systems. In this approach, the Candidate Networks (CNs) from trained keyword queries or executed user queries are classified and stored in the databases, and top-k results from the CNs are learned for constructing CN Language Models (CNLMs). The CNLMs are used to compute the similarity scores between a new user query and the CNs from the query. The CNs with relatively large similarity score, which are the most promising ones to produce top-k results, will be selected and performed. Currently, CLASCN is only applicable for past queries and New All-keyword-Used (NAU) queries which are frequently submitted queries. Extensive experiments also show the efficiency and effectiveness of our CLASCN approach.

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Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, 100872, China

    Jun Zhang, Zhao-Hui Peng, Shan Wang & Hui-Jing Nie

  2. Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education, Beijing, 100872, China

    Jun Zhang, Zhao-Hui Peng, Shan Wang & Hui-Jing Nie

  3. Computer Science and Technology College, Dalian Maritime University, Dalian, 116026, China

    Jun Zhang

Authors
  1. Jun Zhang
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  2. Zhao-Hui Peng
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  3. Shan Wang
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  4. Hui-Jing Nie
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Corresponding author

Correspondence to Jun Zhang.

Additional information

This work is supported by the National Natural Science Foundation of China under Grant Nos. 60473069 and 60496325.

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Zhang, J., Peng, ZH., Wang, S. et al. CLASCN: Candidate Network Selection for Efficient Top-k Keyword Queries over Databases. J Comput Sci Technol 22, 197–207 (2007). https://doi.org/10.1007/s11390-007-9026-6

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  • DOI: https://doi.org/10.1007/s11390-007-9026-6

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