Optimizing Large Scale Ontology Alignment to Establish Interoperability for Efficient Retrieval

The Semantic Web is aiming to add meaning and structure to the vast amount of data available online. It aims to make information machine-readable and interpret, unlike traditional web pages that are designed for human consumption. The increase in adoption of semantic web technologies resulted in the generation of vast number of ontologies with varying size and heterogeneity. The efficient ontology matching system to align the large number of ontologies is extremely required to allow interoperability in cross domain. Although there are various ontology matching systems available which could handle matching small ontologies but when it comes to matching large scale ontologies, high computational and space requirements is always a challenge. In this paper, an ontologies matching system is proposed which partition the large-scale healthcare ontologies in parallel using base level ontology partitioning method and the similarity between the entities are computed using Adjustment similarity computation method. However, the alignment discovery process demands high computational requirement, therefore in this proposed system, the distributed and parallel approach of Map Reduce technique is used to make system more scalable and efficient.