Word sense disambiguation for free-text indexing using a massive semantic network
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
On Measuring Similarity for Conceptual Querying
FQAS '02 Proceedings of the 5th International Conference on Flexible Query Answering Systems
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Matching large schemas: Approaches and evaluation
Information Systems
Matching large ontologies: A divide-and-conquer approach
Data & Knowledge Engineering
AHSCAN: Agglomerative Hierarchical Structural Clustering Algorithm for Networks
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
OSS: a semantic similarity function based on hierarchical ontologies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size
Web Semantics: Science, Services and Agents on the World Wide Web
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The evolution of semantic web leads to more heterogeneity and interoperability problems among the semantic data represented by ontologies. Ontology matching techniques play an important role in semantic web. The ontology matching techniques are used to handle heterogeneity among ontologies and to establish interoperability. The effectiveness of the ontology matching systems is evaluated by the match quality measured in terms of precision and recall. The efficiency of the ontology matching systems depends on the size of the input ontologies (number of concepts in ontology). The size of the ontologies being matched influences the efficiency in terms of execution time and may lead to out of memory error. Hence improving the efficiency of ontology matching system insists on reducing the concept match space which leads to less execution time. The concept match space could be reduced by decomposing the ontologies into disjoint clusters. In this paper, two new neighbour based structural proximity measures TNSP (Tversky based Neighbour Structural Proximity) and DNSP (Dice based Neighbour Structural Proximity) are proposed to form disjoint clusters of the ontology. The proposed measures reduce the number of computations required to identify structurally similar inter ontology concepts thereby improving the efficiency. This reduction in computation is achieved as each concept is compared only with neighbour concepts. The best neighbour combination for the proposed measures TNSP and DNSP is experimentally determined. The proposed measures were evaluated experimentally on real world large ontologies (mouse and human anatomy). The experiments prove that the proposed neighbour based structural similarity measures are more efficient than the existing structural similarity measures without compromising on effectiveness.