Most previous studies of ontology integration have simply involved blind or exhaustive matching among all concepts across ontologies. Therefore, the computational complexity of integrating two ontologies is O(n 2 ). In addition, semantic mismatches, logical inconsistencies and conceptual conflicts in ontology integration have not yet become avoidable. The main contribution of the approach presented here is to reduce the computational complexity and to enhance the accuracy of ontology integration. The key idea of this approach is to start from an Anchor (two matched concepts) to work towards a collection of matched pairs among its neighboring concepts by computing similarities between the “priorly” collected concepts across the ontologies starting from the anchor. The “priorly” means that the PMC, which provides additional suggestions for possible matching concepts, is used to determine for which concepts the similarity should be priorly computed. The algorithm proposed here, based on the idea described above, is called Anchor-Prior algorithm. Experimental comparisons of computational complexity and accuracy with previous approaches are carried out. The results show that the proposed algorithm is effective in terms of both performance (computational time O(n*logn)) and accuracy by avoiding an exponential increase in the number of unmatchable concepts to be checked and by reducing concept mismatches.