Effective keyword search in relational databases

429 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Keyword Search in Relational Databases: A Survey (2010, methodology): “Ranking issues for MTJNTs are discussed in many papers [14, 22, 23].”
BLINKS: ranked keyword searches on graphs (2007, background): “Keyword search on relational databases [1, 3, 17, 16, 23] has attracted much interest.”
Identifying meaningful return information for XML keyword search (2007, background): “There is a lot of work supporting keyword search on a relational database, which is modeled as a graph of objects with connecting edges, DBXplorer [2], BANKS [5], DISCOVER [14], [18].”
Spark: top-k keyword query in relational databases (2007, methodology): “The basic idea of the ranking method used in DISCOVER2 [15] (and its variant [22]) is to 1. assign each tuple in the JTT a score using a standard IR-ranking formula (or its variants); and 2. combine the individual scores together using a score aggregation function, comb ( · ), to obtain the ﬁnal…”
Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data (2009, background): “Much work has been carried out on keyword search over tree-structured data (e.g. [6], [7], [8], [9], [10], [11], [12], [13]) as well as graph-structured data (e.g. [2], [14], [1]).”