Hi all,
I'm currently working on normalizing as many discovery models as possible to eliminate any that are only partially or publisher-normalized. However, for several hundred models, there's little to no available information—certainly not enough to justify manual normalization.
I understand that this lack of data is due to the software not being discovered correctly or completely. That said, has anyone developed any particular methods or strategies to deal with these non-normalized models?
I realize this is fundamentally a discovery issue, but I’m interested in hearing how others have approached similar situations.
Thanks,
Cairns
