Abstract
Most of the existing methods for XML keyword search are based on the notion of Lowest Common Ancestor (LCA). However, as we explore the most important fundamental flaw inside those result models is that the search results are eternally determined and nonadjustable. In order to serve better results, we propose a novel and flexible result model which can avoid all these defects. Within our model, a scoring function is presented to judge the quality of each result. The considered metrics of evaluating results are weighted, and can be updated as needed. Based on the result model, three heuristic algorithms are proposed. Moreover, a mechanism is employed to select the most suitable one out of these algorithms to generate better results. Extensive experiments show that our approach outperforms any LCA-based ones with higher recall and precision.
This research is supported in part by the NSF of China under grant 60773076, the Key Fundamental Research of Shanghai under Grant 08JC1402500, Xiao-34-1, 863 Program of China under Grant 2008AA121706.
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Yang, W., Zhu, H., Li, N., Zhu, G. (2011). Adaptive and Effective Keyword Search for XML. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 6634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20841-6_35
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DOI: https://doi.org/10.1007/978-3-642-20841-6_35
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