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. 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 provides automated query generation components so that, casual users no need to learn the query language in order to perform extraction. To demonstrate the feasibility of our incremental extraction approach, experiments can be performed to highlight two important aspects of an information extraction system: efficiency and quality of extraction results. The existing extraction frameworks do not provide the capabilities of managing intermediate processed data such as parse trees and information. It is also extended to per-sentence extraction, it is important to notice that the query language itself is capable of defining patterns across multiple sentences. Hence, in order to provide nearest and good result the incremental approach is compared with the existing systems.