Share This Article:

Chinese Keyword Search by Indexing in Relational Databases

Abstract Full-Text HTML Download Download as PDF (Size:242KB) PP. 107-112
DOI: 10.4236/jsea.2012.512B021    3,363 Downloads   4,580 Views   Citations


In this paper, we propose a new method based on index to realize IR-style Chinese keyword search with ranking strategies in relational databases. This method creates an index by using the related information of tuple words and presents a ranking strategy in terms of the nature of Chinese words. For a Chinese keyword query, the index is used to match query search words and the tuple words in index quickly, and to compute similarities between the query and tuples by the ranking strategy, and then the set of identifiers of candidate tuples is generated. Thus, we retrieve top-N results of the query using SQL selection statements and output the ranked answers according to the similarities. The experimental results show that our method is efficient and effective.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

L. Zhu, L. Pan and Q. Ma, "Chinese Keyword Search by Indexing in Relational Databases," Journal of Software Engineering and Applications, Vol. 5 No. 12B, 2012, pp. 107-112. doi: 10.4236/jsea.2012.512B021.


[1] S. Agrawal, S. Chaudhuri and G. Das, “DBXplorer: A System for Keyword-Based Search over Relational Da-tabase,” Proceedings of the 18th International Confe-rence on Data Engineering, San Jose, 26 February -1 March 2002, pp. 5-16.
[2] V. Hristidis, L. Gracano and Y. Papakonstantinou, “Efficient IR-style Keyword Search over Relational Databases,” Proceedings of 29th International Conference on Very Large Data Bases, Berlin, 9-12 September 2003, pp. 850-861.
[3] A. Balmin, V. Hristidis and Y. Papakonstantinou: “Objec-tRank: Authority-Based Keyword Search in Databases”, Proceedings of the 30th International Conference on Very large Data Bases, Toronto, 31August-3September 2004, pp. 564 – 575.
[4] Q. Vu, B. Ooi, D. Papadias and A. Tung, “A Graph Method for Keyword-Based Se-lection of the Top-K Databases,” Proceedings of the ACM SIGMOD International Conference on Manage-ment of Data, Vancouver, 10-12 June 2008, pp. 915-926.
[5] F. Liu, C. Yu and W. Meng, A. Chowd-hury, “Effective Keyword Search in Relational Databas-es,” 26th ACM SIGMOD/PODS International Confe-rence on Management of Data/Principles of Database Systems, Chicago, 27-29 June 2006, pp. 563-574.
[6] L. Qin, J. Yu and L. Chang, “Keyword Search in Databases: The Power of RDBMS,” Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, Rhode Island, 29 June-2 July 2009, pp: 681-694.
[7] J. Yu, L. Qin and L. Chang, “Keyword Search in Relational Databases: A Survey,” IEEE Data Eng. Bull. Special Issue on Keyword Search, Vol. 33 No.1, 2010, pp. 67–78.
[8] L. Zhu, Q. Ma and C. Liu, “Semantic-Distance Based Evaluation of Ranking Que-ries over Relational Databases,” J. Intell. Inf. Syst, Vol. 35 No. 3, 2010, pp. 415-445. doi: 10.1007/s10844-009-0116-5.
[9] L. Zhu, Y. Zhu and Q. Ma, “Chinese Keyword Search over Relational Databas-es,” 2010 Second World Congress on Software Engi-neering, Wuhan, 19-20 December 2010, pp. 217-220.
[10] A. Singhal, “Modern Information Re-trieval: A Brief Overview,” IEEE Data Eng, Vol. 24, No. 4, 2001, pp. 35- 43.
[11] A. Singhal, C. Buckley and M. Mitra, “Pivoted Document Length Normalization,” Pro-ceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Informa-tion Retrieval, Zurich, 18-22 August 1996, pp. 21-29.

comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.