Elements of information theory
Elements of information theory
Proceedings of the 17th International Conference on Data Engineering
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
XRANK: ranked keyword search over XML documents
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Efficient keyword search for smallest LCAs in XML databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
XSEarch: a semantic search engine for XML
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Effective keyword search for valuable lcas over xml documents
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Efficient LCA based keyword search in XML data
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Enabling Schema-Free XQuery with meaningful query focus
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient sort-based skyline evaluation
ACM Transactions on Database Systems (TODS)
Reasoning and identifying relevant matches for XML keyword search
Proceedings of the VLDB Endowment
Lowest common ancestors in trees and directed acyclic graphs
Journal of Algorithms
Fast ELCA computation for keyword queries on XML data
Proceedings of the 13th International Conference on Extending Database Technology
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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.