Paper-04 | Reg. No.:20160604|DOI:V5I1P04
Structured Query Generation by Using PTQL As a Solution for Incremental Information Extraction
A Scholar of Singhania University.Department of Computer Science
Improving the ability of computer systems to process text is a significant research Challenge. Many applications are based on partially structured databases, where structured data conforming to a schema is combined with free text. 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 of such an approach is that 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 only a small part of the corpus might be affected. In this project, we describe a novel approach for information extraction in which extraction needs are expressed in the form of database queries, which are evaluated and optimized by database systems. Using database queries for information extraction enables generic extraction and minimizes reprocessing of data by performing incremental extraction to identify which part of the data is affected by the change of components or goals. Furthermore, our approach provides automated query generation components so that casual users do not have to learn the query language in order to perform extraction. To demonstrate the feasibility of our incremental extraction approach, we performed experiments to highlight two important aspects of an information extraction system: efficiency and quality of extraction results.