Learning metadata from the evidence in an on-line citation matching scheme

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