Efficient Approximate Membership Localization using P-Prune algorithm in blogs

Approximate Membership Localization (AML) is concerned with locating non-overlapped substrings thus avoiding redundancies. This overcomes the drawback of Approximate Membership Extraction (AME) process which has low efficiency for real world application. An algorithm called P-Prune is used in Blog search. This prunes most of the overlapped redundant substrings before generating them. Here we use the opinion retrieval scheme which analyses the viewers' comments on Blog contents. Our experimental study on blogs reveals the efficiency of P-Prune over AME method. We also work AML in application to a proposed Blog search framework, a search-based approach joining two tables using dictionary-based entity recognition from blogs. Apart from the advantage of AML over AME, the experiment also proves the efficiency of the search-based approach. This algorithm can be extended to video blogs (vlog) also.