Voting based fuzzy linguistic matching

Schema matching is an essential task in data integration scenarios. The proposed schema matchers can be classified into several categories, one of which is the category of linguistic matchers, which evaluate relatedness comparing the textual elements in the schemata. There are several linguistic matchers the performance of which may vary from scenario to scenario. In order to eliminate the incorrect similarity values, we have implemented a voting based approach. Another contribution presented in this paper is the proposed fuzzy linguistic matching, which involves the transformation of similarity values into membership functions.