This paper addresses a novel task of semantically analyzing the comparative constructions inherent in attributive superlative expressions against structured knowledge bases. We exploit Wikipedia and Freebase to collect training data in an unsupervised manner, where a neural network model is learnt to select, from Freebase predicates, the most appropriate comparison dimensions for a given superlative expression, and further determine its ranking order heuristically. Experimental results show that it is possible to learn from coarsely obtained training data to semantically characterize the comparative constructions involved in superlative expressions.