Schema matching is the problem of finding relationships among concepts across heterogeneous data sources (heterogeneous in format and structure). Schema matching systems usually exploit lexical and semantic information provided by lexical databases/thesauri to discover intra/inter semantic relationships among schema elements. However, most of them obtain poor performance on real world scenarios due to the significant presence of “non-dictionary words”. Non-dictionary words include compound nouns, abbreviations and acronyms. In this paper, we present NORMS (NORMalizer of Schemata), a tool performing schema label normalization to increase the number of comparable labels extracted from schemata.