Abstract
In this paper, we design a tag ranking method to provide multi-level keyword suggestion. The suggested keywords are used to effectively filter query results, which helps users to perform query specialization in social tagging systems. Besides, error-tolerant set containment queries are used to support various degrees of query generalization. We propose an index structure, which aggregates similar tag sets into clusters. A bounding mechanism is provided to efficiently deal with query processing for error-tolerant set containment queries on tag sets. These strategies can be used to support generalizations of a query. A systematic performance study is performed to show the effectiveness and the efficiency of the proposed methods.
This work was partially supported by the R.O.C. N.S.C. under Contract No. 101-2221-E-003-025.
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Koh, JL., Chiang, KT., Chiu, IC. (2013). The Strategies for Supporting Query Specialization and Query Generalization in Social Tagging Systems. In: Hong, B., Meng, X., Chen, L., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40270-8_14
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DOI: https://doi.org/10.1007/978-3-642-40270-8_14
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