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
Keyword Proximity Search in XML Trees
IEEE Transactions on Knowledge and Data Engineering
Multiway SLCA-based keyword search in XML data
Proceedings of the 16th international conference on World Wide Web
Identifying meaningful return information for XML keyword search
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
XSEarch: a semantic search engine for XML
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Effective keyword search for valuable lcas over xml documents
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
XSeek: a semantic XML search engine using keywords
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient LCA based keyword search in XML data
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Race: finding and ranking compact connected trees for keyword proximity search over xml documents
Proceedings of the 17th international conference on World Wide Web
Retrieving meaningful relaxed tightest fragments for XML keyword search
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Fast ELCA computation for keyword queries on XML data
Proceedings of the 13th International Conference on Extending Database Technology
Semantic-Distance based clustering for XML keyword search
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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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.