multilingual extraction and mapping of dictionary entry names in business schema integration
Being a research field for many years, natural language processing (NLP) has gained a lot of attention in recent times due to the quality of translation software like Google Translator or Babylon. In the context of the iGreen project, we are developing a schema integration service (working title: Warp 10), which helps to generate business transformations from individual business schemata. To match the different schemas natural language processing plays an important role. Information coming from user input or files is extracted and mapped to a canonical data model (CDM) based on the CCTS standard. In this paper, we illustrate the use of NLP for the extraction of dictionary entry names (DENs) and indicate some of the problems of NLP and term extraction. Furthermore, we describe the NLP-supported mapping of DENs and outline the problems and approaches in a multilingual setting.
schema integration natural language processing igreen term extraction warp10 schema mapping schema modeling