An efficient method of semantic discovery of enterprise information integration using hybrid schema matching strategy

Having pointed out what we believe to be the deficiencies of past algorithms[4-7], we propose what we believe to be a more efficient one. Section 1 of the full paper briefs how to input our algorithm. Section 2 explains in some detail the principles of our algorithm. Subsections 3.1 and 3.2 explain in some detail the implementation of our algorithm. Its core consists of two phases: (1) during the first phase of compound name matching, our algorithm matches the schemas to be input by running the three linguistic individual matchers of character string matcher, name semantic matcher and domain-specific matcher to obtain the name semantic correlation as shown in Fig. 1 of the full paper; (2) during the second phase of feature classification, our algorithm inputs the names and other constraints of schema elements into self-organizing mapping neural networks, thus achieving their clustering.