Abstract | ||
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A popular approach to hosting Keyword Search Systems (KWS) on relational DBMS platforms is to employ the Candidate Network framework. The quality of a Candidate Network-based search is critically dependent on the scoring function used to rank the relevant answers. In this paper, we first demonstrate, through detailed empirical and conceptual analysis studies, that the Labrador scoring function provides the best user relevance among contemporary Candidate Network scoring functions. |
Year | DOI | Venue |
---|---|---|
2020 | 10.1016/j.is.2019.101465 | Information Systems |
Keywords | Field | DocType |
Keyword search,RDBMS,Operators,Result ranking,Indexes | Codebase,SQL,Data mining,Ranking,Result set,Computer science,Implementation,Database engine,Operator (computer programming),Relational database management system | Journal |
Volume | ISSN | Citations |
89 | 0306-4379 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Vinay | 1 | 0 | 0.34 |
Jayant R. Haritsa | 2 | 2004 | 228.38 |