Keyword searching and browsing in databases using BANKS

By Gaurav Bhalotia, Arvind Hulgeri, Charuta Nakhe, Soumen Chakrabarti, S. Sudarshan. Proceedings / International Conference on Data Engineering, 2002

1,091 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e7efa57069af1e87089a211b375a63e01b6040b8

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Citations in the corpus

Identifying meaningful return information for XML keyword search (2007, methodology): “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].”
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].”
Principles of dataspace systems (2006, background): “Some work has already been done on ranking answers in the context of keyword queries over relational databases and over XML documents [3, 4, 9, 30, 33], and on finding relevant information sources in large collections of formally described sources [41].”
Keyword Search in Relational Databases: A Survey (2010, methodology): “First, all the single source shortest path iterators from the BANKS-I algorithm are merged into a single iterator, called the incoming iterator.”
BLINKS: ranked keyword searches on graphs (2007, methodology): “This process is similar to the keyword query algorithm given by BANKS [3], except that we are creating an index instead of answering online queries.”
Finding Top-k Min-Cost Connected Trees in Databases (2007, background): “The recent studies on supporting IR style queries in RDBMS include DBXPlore [1], IR-Style [17], DISCOVER [18], ObjectRank [4], BANKS-I [6], and BANKS-II [25].”
XRANK: ranked keyword search over XML documents (2003, background): “Some recent work on searching graph databases [4][13] considers a variant of 1 and some part of 3, but does not consider 2.”
Effective keyword search in relational databases (2006, neutral): “In Section”
A survey of community search over big graphs (2019, methodology): “yword Search Generally, graph keyword search [184,205] aims to nd a tree or a subgraph, which contains a set of query keywords, from a large graph G. Earlier studies often output a tree structure. In [21], Bhalotia et al. developed a backward algorithm for nding Steiner trees. In [50], Ding et al. p”
Constructing an Interactive Natural Language Interface for Relational Databases (2014, background): “Keyword search interfaces are widely used by non-experts to specify ad-hoc queries over relational databases [19, 4, 1, 8].”
Schema-free SQL (2014, methodology): “In graph-based approaches, the database is modeled as a data graph, in which each node is a tuple [6].”
EASE: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data (2008, methodology): “Prior works [2] of keyword search over structured data always identify connected trees with minimal cost in the labeled graph as answers.”
Bidirectional Expansion For Keyword Search on Graph Databases (2005, methodology): “This step differs from the formula in [3], which adds up the scores of all edges in a tree.”
Efficient IR-Style Keyword Search over Relational Databases (2003, methodology): “BANKS [2] views a database as a graph where the database tuples (or objects) are the nodes and application-specific “relationships” are the edges.”
Random Walk Inference and Learning in A Large Scale Knowledge Base (2011, neutral): “It has been embodied previously in first-order learning systems (Richards and Mooney, 1992) as well as N-FOIL, and relational database searching systems (Bhalotia et al., 2002).”
DataTone: Managing Ambiguity in Natural Language Interfaces for Data Visualization (2015, neutral): “Keyword search interfaces over databases are another popular approach for specifying ad-hoc queries [1, 3, 11].”