1. A method for entity resolution between traditional and non-traditional datasets, the method comprising: creating, via a computer processor, a deterministic model, the creating comprising: defining an entity to be resolved; selecting two datasets for comparison, wherein a first of the two datasets is a traditional database comprising structured or semi-structured data records, wherein a second of the two datasets is a non-traditional database comprising unstructured data records; defining matching predicates for attributes of the two datasets to select a set of candidate matches; and defining at least one precedence rule for the candidate matches to select a subset of the candidate matches; running, via the computer processor, the deterministic model on the two datasets, the running comprising applying the matching predicates and the at least one precedence rule to data in the two datasets that correspond to the attributes; applying a cardinality rule to results of the running; outputting, via the computer processor, the matching candidates for which the cardinality rule is satisfied; creating a probabilistic model, comprising: selecting records from the two datasets for comparison; implementing data derivation on the records, the data derivation resulting in standardized and bucketed data; defining blocking conditions for application to each of corresponding bucketed data; and defining a scoring rule for pairwise comparisons of the bucketed data; running the probabilistic model on the bucketed data; scoring results of the pairwise comparisons of the bucketed data; and combining the results of the deterministic model with the results of the probabilistic model.
8. The method of claim 1 , wherein one of the matching predicates checks that a frequency of occurrence of an attribute value falls below a specified value.