Abstract
Keyword search has become one of hot topics in the field of information retrieval. It can provide users a simple and friendly interface. But the efficiency of some existing keyword search algorithms is low and there are some draws in sorting results. Most algorithms are suited for either unstructured data or structured data. This paper proposes a new kind of top-k keyword search algorithm. No matter the data is unstructured, semi-structured or structured, the algorithm is always effective. It introduces the concept of neighbor sets of nodes and uses set join algorithm to narrow the search space. We also propose the definition of classified Steiner tree, which can reduce the draw phenomenon in results. In addition, the algorithms can output the results of the classified Steiner tree at the same time of computing them. So it can reduce the waiting time of the users and improve the efficiency of keywords search.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lin, Z., Yang, D., Wang, T.: Keyword Search over Relational Databases. Journal of Software 21(10), 2465–2466 (2010)
Kimelfeld, B., Sagiv, Y.: Finding and approximating top-k answers in keyword proximity search. In: Vansummeren, S. (ed.) Proc. of the 25th ACM SIGACT-SIGMOD-SIGART Symp. on Principles of Database Systems (PODS 2006), pp. 173–182. ACM Press, Chicago (2006)
Liu, F., Yu, C.T., Meng, W.Y., Chowdhury, A.: Effective keyword search in relational databases. In: Chaudhuri, S., Hristidis, V., Polyzotis, N. (eds.) Proc. of the 2006 ACM SIGMOD Int’l Conf. on Management of Data (SIGMOD 2006), pp. 563–574. ACM Press, Chicago (2006)
Ding, B., Yu, J.X., Wang, S., Qin, L., Zhang, X., Lin, X.M.: Finding top-k min-cost connected trees in databases. In: Proc. of the 23rd Int’l Conf. on Data Engineering (ICDE 2007), pp. 836–845. IEEE ComputerSociety Press, Istanbul (2007)
Tao, Y.F., Yu, J.X.: Finding frequent co-occurring terms in relational keyword search. In: Kersten, M.L., Novikov, B., Teubner, J., Polutin, V., Manegold, S. (eds.) Proc. of the 12th Int’l Conf. on Extending Database Technology (EDBT 2009), pp. 839–850. ACM Press, Saint Petersburg (2009)
Kmelfeld, B., Sagiv, Y.: Efficiently enumerating results of keyword search over data graphs. Information System 33(4-5), 335–359 (2008)
Hristidis, V., Papakonstantinou, Y.: DISCOVER: Keyword search in relational databases. In: Proc. of the 28th Int’l Conf. on Very Large Data Bases (VLDB 2002), pp.670–681. Morgan Kaufmann Publishers, Hong Kong (2002)
Luo, Y., Lin, X.M., Wang, W., Zhou, X.F.: Spark: Top-k keyword query in relational databases. In: Chan, C.Y., Ooi, B.C., Zhou, A.Y. (eds.) Proc. of the 2007 ACM SIGMOD Conf. on Management of Data (SIGMOD 2007), pp. 115–126. ACM Press, Beijing (2007)
Markowetz, A., Yang, Y., Papadias, D.: Keyword search on relational data streams. In: Chan, C.Y., OoiBC, Z.A. (eds.) Proc. of the 2007 ACM SIGMOD Conf. on Management of Data (SIGMOD 2007), pp. 605–616. ACM Press, Beijing (2007)
Guo, L., Shao, F., Botev, C., Shanmugasundaram, J.: Xrank: Ranked keyword search over XML documents. In: SIGMOD, pp. 16–27 (2003)
Cohen, S., Mamou, J., Kanza, Y., Sagiv, Y.: Xsearch: Asemantic search engine for XML. In: VLDB (2003)
Kacholia, V., Pandit, S., Chakrabarti, S., Sudarshan, S., Desai, R., Karambelkar, H.: Bidirectional expansion forkeyword search on graph databases. In: VLDB, pp. 505–516 (2005)
Brin, S., Page, L.: The Anatomy of a Large-Scale Hypertextual Web Search Engine. In: WWW Conference (1998)
Arasu, A., Cho, J., Garcia-Molina, H., Paepcke, A., Raghavan, S.: Searching the web. Transactions on Internet Technology (2001)
Li, G.L., Ooi, B.C., Feng, J.H., Wang, J.Y., Zhou, L.Z.: EASE: An effective 3-in-1 keyword search method for unstructured, semi-structuredand structured data. In: Tsong, J., Wang, L. (eds.) Proc. of the 2008 ACM SIGMOD Conf. on Management of Data (SIGMOD 2008), pp. 903–914. ACM, Vancouver (2008)
Qin, L., Yu, J.X., Chang, L., Tao, Y.: Querying communities in relational databases. In: Proc. of ICDE 2009 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, Y., Tang, M., Zhong, Y., Zhang, Z., Guo, L. (2012). An Effective Top-k Keyword Search Algorithm Based on Classified Steiner Tree. In: Bao, Z., et al. Web-Age Information Management. WAIM 2012. Lecture Notes in Computer Science, vol 7419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33050-6_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-33050-6_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33049-0
Online ISBN: 978-3-642-33050-6
eBook Packages: Computer ScienceComputer Science (R0)