Abstract
Identifying relevant results is a key task in XML keyword search (XKS). Although many approaches have been proposed for this task, effectively identifying results for XKS is still an open problem. In this paper, we propose a novel approach for identifying relevant results for XKS by adopting the concept of Mutual Information and skyline semantics. Specifically, we introduce a measurement to effectively quantify the relevance of a candidate by using the concept of Mutual Information and provide an effective mechanism to identify the most relevant results amongst a large number of candidates by using skyline semantics. Extensive experimental studies show that in overall our approach is more effective than existing approaches and can identify relevant results and top k results in acceptable computational costs.
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Nguyen, K., Cao, J. (2010). Relevant Answers for XML Keyword Search: A Skyline Approach. In: Chen, L., Triantafillou, P., Suel, T. (eds) Web Information Systems Engineering – WISE 2010. WISE 2010. Lecture Notes in Computer Science, vol 6488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17616-6_20
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DOI: https://doi.org/10.1007/978-3-642-17616-6_20
Publisher Name: Springer, Berlin, Heidelberg
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