  An author name disambiguation (AND) algorithm identifies a unique author
entity record from all similar or same publication records in scholarly or
similar databases. Typically, a clustering method is used that requires
calculation of similarities between each possible record pair. However, the
total number of pairs grows quadratically with the size of the author database
making such clustering difficult for millions of records. One remedy for this
is a blocking function that reduces the number of pairwise similarity
calculations. Here, we introduce a new way of learning blocking schemes by
using a conjunctive normal form (CNF) in contrast to the disjunctive normal
form (DNF). We demonstrate on PubMed author records that CNF blocking reduces
more pairs while preserving high pairs completeness compared to the previous
methods that use a DNF with the computation time significantly reduced. Thus,
these concepts in scholarly data can be better represented with CNFs. Moreover,
we also show how to ensure that the method produces disjoint blocks so that the
rest of the AND algorithm can be easily paralleled. Our CNF blocking tested on
the entire PubMed database of 80 million author mentions efficiently removes
82.17% of all author record pairs in 10 minutes.
