Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-17616-6_20 | WISE |
Keywords | Field | DocType |
skyline approach,novel approach,effective mechanism,mutual information,key task,skyline semantics,large number,relevant result,extensive experimental study,xml keyword search,acceptable computational cost,relevant answer | Skyline,Data mining,Open problem,XML,Information retrieval,Computer science,Keyword search,Mutual information,Database,Semantics | Conference |
Volume | ISSN | ISBN |
6488 | 0302-9743 | 3-642-17615-1 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Khanh Nguyen | 1 | 128 | 10.39 |
| Jinli Cao | 2 | 517 | 45.22 |