This paper compares four algorithms for computing feature-based similarities between concepts respectively possessing a distinctive set of features. The eventual purpose of comparing these feature-based similarity algorithms is to identify a candidate term in a Target Language (TL) that can optimally convey the original meaning of a culturally-specific Source Language (SL) concept to a TL audience by aligning two culturally-dependent domain-specific ontologies. The results indicate that the Bayesian Model of Generalization [1] performs best, not only for identifying candidate translation terms, but also for computing probabilities that an information receiver successfully infers the meaning of an SL concept from a given TL translation.