An Adaptive approach for Identifying Attribute value for Annotated Document

Abstract

Many organizations generate and use various similarly matching content document descriptions. Such large collections of related documents contain significant amounts of both structured and unstructured information buried in large texts. Previously various information extraction algorithms smoothens the extraction of the relevant relations, they often suffer with huge workloads and falls short of inaccuracies especially when processing a notational text that does not share any similarities with the targeted document contents. Previously proposed query value content value processing algorithms facilitate the annotation of the documents structured information by identifying and extracting the information of interests to aid in subsequent querying for information retrievals. But these approaches fall short of supporting auto suggestions and we propose to extend the querying engine and usage of document annotation strategies with respect to query value and content value for auto attribute suggestions. Our major contribution in this paper, involves in presenting a skyline sweeping algorithm which identifies related content that can be used for auto suggestions and are definite to appear within the document content, by utilizing Meta information and text of the document at reduced query workloads. Our experimental query engine demonstrates that our approach generates efficient results compared to prior approaches in processing attributes of interest.