Gauzy knowledge sharing in conspiring environment using text mining

Abstract

Objective: The people all over the world are in need of gaining knowledge which is achieved by means of searching information through web services. Even though search results obtained from web services are relevant, the user is trying to obtain the best information providers. Methods: The existing approaches which is non-parametric generative model and infinite Hidden Markov Model (iHMM) of clustering technique does not yield better results for very large collections of web services. The iHMM model does not provide any security for datasets in a hierarchal structure. Thus to overcome such downsides of existing approach to a new technique can be used to help of Natural Language Processing (NLP). Findings: The new approach provides both tag recommendation and parental control. The tag recommendation is used to ping the best tags based on expert's bookmarks, and parental control provided by group owner is used to resolve the privacy issues. The web filter denies the unauthorized access. Thus, using NLP technique in the conspiring environment enables the web user to achieve fine grained knowledge. Applications: The paper defines the efficient way to provide tag recommendation on the web for large dataset users.