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
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
Reasoning and identifying relevant matches for XML keyword search
Proceedings of the VLDB Endowment
Identifying relevant matches with NOT semantics over XML documents
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
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Keyword search is a friendly mechanism for the end user to identify interesting nodes in XML databases, and the SLCA (smallest lowest common ancestor)-based keyword search is a popular concept for locating the desirable subtrees corresponding to the given query keywords. However, it does not evaluate the importance of each node under those subtrees. Liu and Chen proposed a new concept contributor to output the relevant matches instead of all the keyword nodes. In this paper, we propose two methods, MinMap and SingleProbe, that improve the efficiency of searching the relevant matches by avoiding unnecessary index accesses. We analytically and empirically demonstrate the efficiency of our approaches. According to our experiments, both approaches work better than the existing one. Moreover, SingleProbe is generally better than MinMap if the minimum frequency and the maximum frequency of the query keywords are close.