Dynamic de-duplication of bibliographic data for user services
Los
Alamos National Laboratory, Research Library
DLF
Forum, October 26 2004, Baltimore, MD
Netrics @ LANL : Conclusions
Results look much better than
those of batch de-duplication approach ~ Netrics matching + training by
librarians
Can de-dup external
data against local data
No batch processing,
but on-the-fly de-duplication
Possibility
to retrain the system to optimize responses without data reprocessing:
machine learning module
Modularity of solution
accommodates growth of dataset
Netrics module can be
used by multiple applications, not just one
Positive collaboration
with Netrics (machine learning, cache)
Will be plugged into
new search environment that is being created
Will be applied to
full-content collection