Automated Creation of Mappings Between Data Specifications Through Linguistic and Structural Techniques.

The ability to perform automated conversions between different data formats is key to achieving interoperability between heterogeneous systems. Conversions require the definition of mappings between concepts of separate data specifications, which is typically a difficult and time-consuming task. In this article, we present a technique that exploits, in part, semantic web technologies to automatically suggest mappings to users based on both linguistic and structural similarities between terms of different data specifications. In addition, we show how a machine-learned linguistic model created by gathering data from domain-specific sources can help increase the accuracy of the suggested mappings. The approach has been implemented in our prototype tool, SMART (SPRINT Mapping & Annotation Recommendation Tool), and it has been validated through tests using specifications from the transportation domain.