This paper proposes Fria, a fast and robust instance alignment framework across two independently built knowledge bases (KBs). Our objective is two-fold: (1) to design an effective instance similarity measure and (2) to build a fast and robust alignment framework. Specifically, Fria consists of two-phases. Fria first achieves high-precision alignment for seed matches which have strong evidence for aligning. To obtain high-recall alignment, Fria then divides non-matched instances according to the types identified from seeds, and gives additional chances to the same-typed instances to be matched. Experimental results show that Fria is fast and robust, by achieving comparable accuracy to state-of-the-arts and a 10-times speed up.