Algorithm: Super-concept-Extraction Input: Ontology A, Ontology B Output: Vector CSA, Vector CSB BEGIN (i) Declare a vector two CSA and CSB to store concepts along with their super-concepts of A and B respectively; where CSA and CSB are of structure type with two elements, one for concept and other for its respective super-concept. 

(ii) Populate a vector *CSA* from each concept and its immediate super-concept of A. 

(iii) FOR each cA in VA 
 superVector = CSA.getSupers(cA) CSA.add(cA , superVector) 
 NEXT 
(iv) Populate a vector CSB from each concept and its immediate superconcept of B. 

(v) FOR each cB in VB 
 superVector = CSB.getSupers(cB) 
 CSB.add(cB, superVector) NEXT END 
Fig 1. Super-concepts extraction from Ontologies A and B 
A) and CSB(Concepts along with Super-concepts of ontology B) are obtained as output of this step.

## Step 3: Matching

 We claim that ontology alignment based on similarities of external structure of concepts need only the similarity of at least on super-concept (immediate is not necessarily) from both concepts involved in matching process. Therefore in this step, each super-concept of cA presented in VA is compared with each super-concept of cBi for all 1≤ i≥ n. If there is a match between any super-concept of cA and any super-concept of cB then both cA and cB are declared similar concepts. A working of matching process in pseudo form is repented in Fig 2. The output e.g. SimilarPair Vector will consist of only those pairs of concepts having similarities from both ontolgies. 

## 4. Case Study

The evaluation of proposed technique was performed automatically and manually with three test cases: (a) Both the source ontology A and the target ontology B belongs to same domain but are developed separately with different viewpoints. (b) Both the source ontology A and the target ontology B belongs to different domains with no similarity. (c)Using same ontology as the source ontology A and the target ontology B. The algorithms listed in the previous section were implemented in Java programming language. A pair of ontologies *developed by different persons* were used for evaluation. In first pair (see Fig.3), both ontologies were about "Research Activities" domain conducted in a university. Workings of proposed technique were also traced manually, using same pairs of ontologies. Then the results obtained through automatically and manually were compared with each other and with expected results mentioned by respective domains experts. We found that results were absolutely correct and complete. 