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XRANK: ranked keyword search over XML documents
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Source International Conference on Management of Data archive
Proceedings of the 2003 ACM SIGMOD international conference on Management of data table of contents
San Diego, California
SESSION: XML and text table of contents
Pages: 16 - 27  
Year of Publication: 2003
ISBN:1-58113-634-X
Authors
Lin Guo  Cornell University
Feng Shao  Cornell University
Chavdar Botev  Cornell University
Jayavel Shanmugasundaram  Cornell University
Sponsor
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 21,   Downloads (12 Months): 182,   Citation Count: 101
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ABSTRACT

We consider the problem of efficiently producing ranked results for keyword search queries over hyperlinked XML documents. Evaluating keyword search queries over hierarchical XML documents, as opposed to (conceptually) flat HTML documents, introduces many new challenges. First, XML keyword search queries do not always return entire documents, but can return deeply nested XML elements that contain the desired keywords. Second, the nested structure of XML implies that the notion of ranking is no longer at the granularity of a document, but at the granularity of an XML element. Finally, the notion of keyword proximity is more complex in the hierarchical XML data model. In this paper, we present the XRANK system that is designed to handle these novel features of XML keyword search. Our experimental results show that XRANK offers both space and performance benefits when compared with existing approaches. An interesting feature of XRANK is that it naturally generalizes a hyperlink based HTML search engine such as Google. XRANK can thus be used to query a mix of HTML and XML documents.


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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L. Guo, F. Shao, C. Botev, J. Shanmugasundaram, "XRANK: Ranked Keyword Search Over XML Documents", Cornell University Technical Report, 2003.
 
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CITED BY  101

Collaborative Colleagues:
Lin Guo: colleagues
Feng Shao: colleagues
Chavdar Botev: colleagues
Jayavel Shanmugasundaram: colleagues