Content Based Segregation of Pertinent Documents Using Adaptive Progression

Abstract

Due to the emerging technology era, today a number of firms share their service/product descriptions. Such a group of information in the textual form has some structured information, which is beneath the unstructured text. A new attainment which facilitates the form of a structured metadata by recognizing documents which are likely to have some type and this information is then used for both segregation and search process. The idea of this advent describes some attributes of a text that will match with the query object which acts as identifier both for segregation as well as for storage and retrieval. An adaptive technique is proposed to deal with relevant attributes to annotate a document by satisfying the users querying needs. The solution for annotation-attribute suggestion problem is not based on the probabilistic model or prediction but it is based on the basic keywords that a user can use to query a database to retrieve a document. Experiment results show that Querying value and Content Value approach is much useful in predicting a tag for a document and thus prediction is also based on Querying value and Content value which greatly improves the utility of shared data which is a drawback in the existing system. This approach is different, as we consider only the basic keywords to be matched with the content of a document. When compared with other approaches in the existing system, Clarity is a primary goal as we expect that the annotator may improve the annotations on process. The discovered tags assist on quest of retrieval as an alternative to bookmarking.