An Improved Matching Algorithm for Developing a Consistent Knowledge Model across Enterprises Using SRS and SWRL

This paper highlights an end-to-end framework and process methodology for developing a consistent knowledge model across enterprises. We demonstrate an improved matching algorithm i.e. the Semantic Relatedness Scores (SRS) and the Semantic Web Rule Language (SWRL) and how they can be coupled together to achieve better reliability and precision in matching heterogeneous data schemas. We introduce a process methodology support this. The goal here is to develop a consistent knowledge model across enterprises that are more precise and reliable. We have also implemented a multi-agent system (MAS) prototype based on the service oriented architecture (SOA) for proof-of-concept. Finally we demonstrate how our approach is represented in the Zachman Framework.