An Ontology-Based Framework for Semi-Automatic Schema Integration

Currently, schema integration frameworks use approaches like rule-based, machine learning, etc. This paper presents an ontology-based wrapper-mediator framework that uses both the rule-based and machine learning strategies at the same time. The proposed framework uses global and local ontologies for resolving syntactic and semantic heterogeneity, and XML for interoperability. The concepts in the candidate schemas are merged on the basis of the similarity coefficient, which is calculated using the defined rules and the prior mappings stored in the case-base.