Abstract
Record linkage offers wide role in record identification and relevant datasets matching. The conventional researchers use probabilistic approach to identify reliable and unique datasets. Record linkage with probabilistic approach exploits data, which are common to an individual record pair. Classical methods have equality based record linkage in common fields. Therefore, errors associated with record linkage reduce the scalability. In this paper, a similarity between individual values of record pairs is improved using ontology-based semantic similarity model. Semantic similarity between the records is tested successfully using angle based neighborhood graph. To validate the proposed approach, a conventional record linkage algorithm is compared with angle based neighborhood ontology record linkage technique, which achieves improved accuracy and scalability. Finally, the accuracy of identifying similar semantic matches is more scalable in proposed technique than conventional methods.
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