The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Keyword searching and browsing in databases using BANKS
Abstract
With the growth of the Web, there has been a rapid increase in the number of users who need to access online databases without having a detailed knowledge of the schema or of query languages; even relatively simple query languages designed for non-experts are too complicated for them. We describe BANKS, a system which enables keyword-based search on relational databases, together with data and schema browsing. BANKS enables users to extract information in a simple manner without any knowledge of the schema or any need for writing complex queries. A user can get information by typing a few keywords, following hyperlinks, and interacting with controls on the displayed results. BANKS models tuples as nodes in a graph, connected by links induced by foreign key and other relationships. Answers to a query are modeled as rooted trees connecting tuples that match individual keywords in the query. Answers are ranked using a notion of proximity coupled with a notion of prestige of nodes based on inlinks, similar to techniques developed for Web search. We present an efficient heuristic algorithm for finding and ranking query results.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Dynamic queries for visual information seeking
Shneiderman, B. (1994) · IEEE Software · doi:10.1109/52.329404 · Cited by 573
HyPursuit: a hierarchical network search engine that exploits content-link hypertext clustering
Weiss, R., Vélez, B., & Sheldon, M. A. (1996) · Proceedings of the the seventh ACM conference on Hypertext - HYPERTEXT '96 · doi:10.1145/234828.234846 · Cited by 209
Goldman, R., Shivakumar, N., Venkatasubramanian, S., et al. (1998) · Cited by 197
Retrieving and organizing web pages by “information unit”
Li, W., Candan, K. S., Vu, Q., & Agrawal, D. (2001) · International World Wide Web Conference (WWW) · doi:10.1145/371920.372057 · Cited by 107
Cited by
A survey of community search over big graphs
Fang, Y., Huang, X., Qin, L., Zhang, Y., Zhang, W., Cheng, R., & Lin, X. (2020) · The VLDB Journal · doi:10.1007/s00778-019-00556-x · Cited by 339
SEA: Segment-enrich-annotate paradigm for adapting dialog-based content for improved accessibility
Candan, K. S., Dönderler, M. E., Hedgpeth, T., Kim, J. W., Li, Q., & Sapino, M. L. (2009) · ACM Transactions on Information Systems · doi:10.1145/1508850.1508853 · Cited by 8
Metadata
- Authors
- Bhalotia, G., Hulgeri, A., Nakhe, C., Chakrabarti, S., & Sudarshan, S.
- Venue
- Proceedings 18th International Conference on Data Engineering
- Publisher
- IEEE Comput. Soc
- Published
- 2003-06-25
- Pages
- 431-440
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W2169624745
- Cited by
- 871
- References
- 5
- FWCI
- 16.44
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Keyword searching and browsing in databases using BANKS. https://app.cochat.ai/papers/10.1109/icde.2002.994756
- The paper
- Bhalotia, G., Hulgeri, A., Nakhe, C., Chakrabarti, S., & Sudarshan, S. (2003). Keyword searching and browsing in databases using BANKS. Proceedings 18th International Conference on Data Engineering. https://doi.org/10.1109/icde.2002.994756
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.