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An effective and versatile keyword search engine on heterogenous data sources
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Proceedings of the VLDB Endowment archive
Volume 1 ,  Issue 2  (August 2008) table of contents
SESSION: Demonstrations: web, textual data table of contents
Pages 1452-1455  
Year of Publication: 2008
ISSN:2150-8097
Authors
Guoliang Li  Tsinghua University, Beijing, P.R. China
Jianhua Feng  Tsinghua University, Beijing, P.R. China
Jianyong Wang  Tsinghua University, Beijing, P.R. China
Lizhu Zhou  Tsinghua University, Beijing, P.R. China
Publisher
Bibliometrics
Downloads (6 Weeks): 3,   Downloads (12 Months): 98,   Citation Count: 1
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ABSTRACT

We present EASE, an effective and versatile keyword search engine that enables users to easily access the heterogenous data composed of unstructured, semi-structured and structured data, without the need of learning XPath/XQuery or SQL languages. EASE addresses a challenge in keyword search that has been neglected in the literature: how to efficiently and adaptively process keyword queries on the heterogenous data. To provide such capability, EASE models unstructured, semi-structured and structured data as graphs, summarizes the graphs, and constructs graph indices instead of using traditional inverted indices for effective keyword search. EASE adopts an extended inverted index to facilitate keyword-based search, and employs a novel ranking mechanism for enhancing search effectiveness.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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G. Li, J. Feng, J. Wang, and L. Zhou. Efficient keyword search over data-centric xml documents. In APweb, 2007.
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G. Li, J. Feng, and L. Zhou. Finding dominate trees for effective keyword search over relational databases. In BNCOD, 2008.
 
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G. Li, J. Feng, and L. Zhou. Retune: Retrieving and Materializing Tuple Units for Effective Keyword Search over Relational Databases. In ER, 2008.
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Collaborative Colleagues:
Guoliang Li: colleagues
Jianhua Feng: colleagues
Jianyong Wang: colleagues
Lizhu Zhou: colleagues