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Learning metadata from the evidence in an on-line citation matching scheme
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Source International Conference on Digital Libraries archive
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries table of contents
Chapel Hill, NC, USA
SESSION: Information retrieval 2 table of contents
Pages: 276 - 285  
Year of Publication: 2006
ISBN:1-59593-354-9
Authors
Isaac G. Councill  The Pennsylvania State University, University Park, PA
Huajing Li  The Pennsylvania State University, University Park, PA
Ziming Zhuang  The Pennsylvania State University, University Park, PA
Sandip Debnath  The Pennsylvania State University, University Park, PA
Levent Bolelli  The Pennsylvania State University, University Park, PA
Wang Chien Lee  The Pennsylvania State University, University Park, PA
Anand Sivasubramaniam  The Pennsylvania State University, University Park, PA
C. Lee Giles  The Pennsylvania State University, University Park, PA
Sponsors
ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 4,   Downloads (12 Months): 54,   Citation Count: 1
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ABSTRACT

Citation matching, or the automatic grouping of bibliographic references that refer to the same document, is a data management problem faced by automatic digital libraries for scientific literature such as CiteSeer and Google Scholar. Although several solutions have been offered for citation matching in large bibliographic databases, these solutions typically require expensive batch clustering operations that must be run offline. Large digital libraries containing citation information can reduce maintenance costs and provide new services through efficient online processing of citation data, resolving document citation relationships as new records become available. Additionally, information found in citations can be used to supplement document metadata, requiring the generation of a canonical citation record from merging variant citation subfields into a unified "best guess" from which to draw information. Citation information must be merged with other information sources in order to provide a complete document record. This paper outlines a system and algorithms for online citation matching and canonical metadata generation. A Bayesian framework is employed to build the ideal citation record for a document that carries the added advantages of fusing information from disparate sources and increasing system resilience to erroneous data.


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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Collaborative Colleagues:
Isaac G. Councill: colleagues
Huajing Li: colleagues
Ziming Zhuang: colleagues
Sandip Debnath: colleagues
Levent Bolelli: colleagues
Wang Chien Lee: colleagues
Anand Sivasubramaniam: colleagues
C. Lee Giles: colleagues