Federated query processing using ontology structure and ranking in a service oriented environment

In view of the need for highly distributed and federated architecture, ranking data sources (ontologies) and robust query expansion in a specific domain have great impact on the performance and accuracy of web applications. Since robust query expansion exploits multiple data sources (ontologies) instead of a single ontology, ontology ranking is considered as a precursor for robust query expansion. Ontology ranking determines quality of an ontology and commonality of overlapping entities across different ontologies of the same domain. For this, first, we calculate the similarity of ontologies by an Entropy Based Distribution (EBD) measurement based on commonality of overlapping entities. Next, we determine robust expansion terms by a number of semantic measures. We consider each individual ontology and user query keywords to determine the Basic Expansion Terms (BET) based on the structure of ontology. We use Density Measure (DM), Betweenness Measure (BM), Semantic Similarity Measure (SSM) and Weight of Semantic Path (WSP) to calculate BET. Then, we specify New Expansion Terms (NET) using the ontology alignment (OA). Further, we determine the Robust Expansion Term (RET) using a dynamic threshold. We propose Map-Reduce algorithms for computing all the above metrics to make our algorithms efficient and scalable. Ontology rank is used as a heuristic to determine dynamic threshold and k-top relevant terms for each user query. Finally, we compare the result of our novel ontology-driven expansion approach with another existing semantic widget as well as wordnet and show the effectiveness of our robust expansion in federated architecture.