Extraction of incremental information using Query Evaluator

Abstract

Biomedical information is growing explosively, and new and useful results are appearing daily in research publications. However, automatic extraction of useful information from such a huge amount of documents remains a challenge because these documents are unstructured and expressed in a natural language form. To enable data mining and knowledge discovery from such documents, this data must be made available in a structured format. Information extraction is the process of scanning text for information relevant to some interest, including extracting entities, relations and events. It requires deeper analysis than key word searches. Many automatic Information Extraction approaches using Natural Language Processing and Text Mining technologies have been proposed to extract automatically meaningful information in Biomedical domain. The Information Extraction system recognizes and extracts knowledge from a massive literature and the extracted knowledge is accumulated in a knowledge base. A major drawback of conventional information extraction system 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. This work describe a approach for incremental information extraction in which extraction needs are expressed in the form of database queries. This work aims that in the event of deployment of a new module, incremental extraction approach reduces the processing time compared to a conventional approach.