Concept Based Ontology Matching By Concept Enrichment

One of the important barrier that hinders achieving semantic interoperability is ontology matching. Instancebased ontology matching (IBOM) uses the extension of concepts, the instances directly associated with a concept, to determine whether a pair of concepts is related or not. In practice, however, instances are often associated with concepts of a single ontology only, rendering IBOM rarely applicable. This is achieved by enriching instances of each dataset with the conceptual annotations of the most similar instances from the other dataset, creating artificially dually annotated instances. We call this technique instance-based ontology matching by instance enrichment (IBOMbIE). We are using the instance matching process with web crawlers mediating four world’s leading publishers such as Willey, Oxford, ScienceDirect and Springer. We are obtaining keywords from the articles of these four journals which acts as the instances. We are particularly considering ARTIFICIAL INTELLIGENCE and COMPUTER NETWORKS since these four journals consists of huge database regarding articles within it. After searching and finding keywords those instances are matched with their ontology creation and further enrichment of instances. Through this technique we will obtain instances that are uncommon among two datasets.