Journal of Computing, Volume 2, Issue 6, June 2010, ISSN 2151-9617 HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING/ WWW.JOURNALOFCOMPUTING.ORG 148 Our proposed technique computes the similarity between concept Ci of Ontology A and concept Cj of ontology B, 
based on the criteria: (i) At least one of the super-concepts of Ci and Cj must be similar; (ii) Similarity between subconcepts of Ci and Cj is optional; (iii) Similarity between siblings of Ci and Cj is optional; (iv) Similarity between non-taxonomic relations of both concepts in their ontologies is also optional. 

However, to rank the level of similarity between those concepts or to measure their granularity level the options 
(ii), (iii) and (iv) may be considered. Our proposed technique is more generic, simple and efficient because when two ontologies are aligned using this technique, none of similar pair of concepts remains unaligned. However existing techniques may leave some similar concepts unaligned. Moreover, the computing of optional similarities such as for sub-concepts, sibling concepts and non-taxonomic relations of both concepts involved in similarity computing process are omitted in this technique. The remaining paper is organized as follows: In Section 2 an overview of some existing techniques is given. The proposed technique is given in Section 3 and it is illustrated by a case study in Section 4. The results generated through our technique and some existing techniques are compared in the same Section. Finally, the paper is concluded with future work in Section 5. 

## 2. Related Work

Semantic web heavily relies on ontologies. When ontologies need to be merged, integrated, queried and are used for knowledge sharing, their heterogeneity becomes bottleneck. To resolve this problem these need to be aligned [3, 9]. For alignment, the semantic similarities among the concepts of ontologies are determined. While determining similarities; the semantics, structural, taxonomical and contextual perspectives of concepts are taken into consideration [5]. The structure-level techniques for ontology alignment compare similarity of concepts based on different criteria as listed in previous Section. According to [8], two concepts are similar if their direct super-entities are similar. In [11],[12],[13],[14] it is stated that two concepts are similar if their direct super-entities are similar, their sibling-entities are similar; their direct sub-entities are similar; their descendant-entities are similar; their leaf-entities are similar and entities in the paths from the root to those concepts are similar. 

In [15], the structural similarity between two concepts is computed from the average similarity of their respective super and sub-concepts. The super and sub concepts of two concepts being compared are fetched into separate sets and then the resultant similarity is computed from the similarities of concepts in those sets. If both the super and sub concepts similarity are undefined, then the concepts being compared are declared as dissimilar otherwise they are declared similar concepts. These techniques need to be more generic because for certain scenario these techniques are not suitable. For example, concepts of two ontologies are still similar (see Fig. 6 and Fig 8 in Sec. 4), although their super concepts, sibling-concepts, sub-concepts, descendent-concepts, leafconcepts and concepts from root to those concepts are not similar. Furthermore, it is recommended that structural equality is not sufficient for measuring the alignment. 

Instead the concepts should be aligned on the basis of their semantics equality [15]. We think that these criteria should be more generic for computing similarities in two concepts.

## 3. Proposed Technique

As mentioned earlier that our proposed technique computes the similarity between concepts of two ontologies based on criteria. At least one super-concept from both concepts involved in the matching process must be similar. There is no need of finding similarity between sub-concepts, siblings and their interactions with other concepts in their respective ontologies. Here we include only algorithm for extracting super-concepts and their matching. Algorithms for matching the siblings-concepts, sub-concepts and other interacting objects of both concepts involved in similarity measuring process are omitted here because according to our proposed criteria, all these are optional. However to rank the level of similarity, we are also working on these aspects. 

Our proposed technique works in three steps: (i) Concepts Extraction (ii) Super-concepts Extraction (iii) Matching. 

This technique works with assumption that both ontologies are defined in Web Ontology Language (OWL) [16]. 

## Step 1: Concepts Extraction

As stated earlier, only concepts are involved in alignment process based on external-structure whereas properties are involved in internal-structure alignment process. Therefore we extract concepts only and there is no need of extracting properties. Both source and target ontologies involved in alignment process are parsed and the concepts presented in 
"owl:CLASS" tag are extracted for determining their similarities. This step is very simple. Two vectors VA and VB are declared and then are populated from concepts of ontologies A and B respectively. 

## Step 2: Super-Concepts Extraction

 Since the proposed criterion is based on the comparison of super-concepts for both concepts involved in the matching process, therefore for both ontologies we need all super-concepts of each concept extracted in previous step. Working of this step in pseudo form is given in Fig 1. Vectors named as CSA (Concepts along with Super-concepts of ontology 