Automated Query Generation of RDBMS for Information and Knowledge Extraction
Information extraction systems traditionally implemented as a pipeline of special-purpose processing modules targeting the extraction of a particular kind of information. Most recent IE approaches are suitable for only static corpora. A major drawback is, whenever a new extraction goal emerges or a module is improved, extraction has to be reapplied from scratch to the entire text corpus even though a small part of corpus might be affected. By using database queries, information extraction enables the generic extraction and minimizes re-processing of data. Furthermore, this paper provides automated query generation and performance extraction. To demonstrate the feasibility of the authors' incremental extraction approach, experiments can be performed to highlight two important aspects of an information extraction system: efficiency and quality of extraction results.