Bidirectional Expansion For Keyword Search on Graph Databases

By Varun Kacholia, Shashank Pandit, Soumen Chakrabarti, S. Sudarshan, Rushi Desai, Hrishikesh Vijay Karambelkar. Very Large Data Bases Conference, 2005

595 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5ccef1ea4c8d0c7cc75545ad765cc810e69b4223

How researchers see this paper

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

Citations in the corpus

Spark: top-k keyword query in relational databases (2007, methodology): “There has been many related work dedicated to keyword search in databases recently [12, 1, 16, 15, 3, 18, 19, 20, 22].”
BLINKS: ranked keyword searches on graphs (2007, methodology): “The most related work, BANKS [3, 18], has been discussed extensively in Section 3.”
Finding Top-k Min-Cost Connected Trees in Databases (2007, methodology): “We conducted extensive experimental studies to compare our parameterized solution, DPBF, with four algorithms, namely, BANKS-I [6], BANKS-II [25], RIU-E [22], and RIU-T [22].”
Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data (2009, methodology): “Since book-keeping this information is difficult and expensive, current algorithms compute the best answers only in an approximate way (see [1], [14]).”
Keyword Search in Relational Databases: A Survey (2010, background): “BLINKS [13] is proposed as a bi-level index to speed up BANKS-II, as no index (except the keyword-node index) is used in BANKS-II.”
Effective Community Search for Large Attributed Graphs (2016, background): “Given an attributed graph G and a set Q of keywords, graph keyword search solutions output a tree structure, whose nodes are vertices of G, and the union of these vertices’ keyword sets is a superset of Q [3, 6, 16].”
Comparing Stars: On Approximating Graph Edit Distance (2009, background): “For example, a variety of eﬀective algorithms have been devised to mine graph patterns(e.g., frequent patterns) from graph databases[33], to index graph databases for eﬃciently processing graph search[13, 37, 9] and to perform keyword search over graph databases[14, 17, 30].”
EASE: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data (2008, background): “[17] presented the bidirectional strategy (BANKS-II) to improve the efficiency of keyword search over graph data.”