A weighted graph-oriented ontology matching algorithm for enhancing ontology mapping and alignment in Semantic Web

Ontologies support the intelligent systems in which information needs to be handled, reused and transferred. This paper is about a weighted graph-oriented ontology matching algorithm, namely the WeGO++ algorithm that helps ontology matching in a pragmatic manner. It considers predominantly the graph-structured nature of ontology models. The methods and approaches in obtaining matching across ontologies use the basic principles of similarity identifications among graph structures. WeGO++ ontology matching frame work is able to successfully match all the data sets. The algorithmic process that is contained in the work looks for nine types of Similarity Scores between members of the ontologies to be matched. Each score is given a specific weight for arriving at the final Similarity Score for each element, and the overall Similarity Score for the ontology is computed using these values. The pragmatic nature of this matching solution is an advantage which approaches the task in a simplistic and efficient manner. The computations are not very complicated and do not use large computing power. It is possible to add more features such as incorporating a sophisticated GUI, changing the matching threshold at runtime and adding a new module to introduce a graphical representation of Similarity Scores of the ontology elements. Threshold values can be fixed to accept levels of matching between elements and classify the quality of match that exists between the ontologies concerned. This provides some room for the manual decision making which can be efficient in deciding matching levels in certain hesitant matches. The algorithms have been tested with OAEI test data sets and some popular ontology library data sets apart from ontologies built from conventional database data sets.