Journal of Computer Science

Unified Semantic Blog Mining Framework and Summarized Blog Retrieval

Godfrey Winster Sathianesan and Swamynathan Sankaranarayanan

DOI : 10.3844/jcssp.2013.207.217

Journal of Computer Science

Volume 9, Issue 2

Pages 207-217


In today’s scenario, publishing the personal content and technical content has become very easy for the publishers, because of the readily available prominent social media called blog. Content developers, teachers, researchers post various articles relevant to their research or newly emerging topics. Blog content in one blog source may resemble the semantics of blog from other source. Readers, who are fascinated in reading the blog content, anticipate retrieving the blogs from different sources for their query. A large number of posts are available in the web. Hence the blog reader’s task becomes very complex to search the relevant content for their query. This study introduces a novel idea to collect the blog using unified Semantic Blog Mining Framework (SBMF). SBMF collects blogs from different blog sources using the ontology constructed for education domain. Blogs collected from different sources are collection which contains relevant or irrelevant blogs. The new blog summarizer summarizes the blog content and ranks the blogs according to the similarity of the blog with query given by the user. The proposed blog summarizer check the similarity of each sentence in a blog, sort the order of sentence based on the similarity of the text with query word and reduces the number of sentences. The experimental results shows that the proposed unified SBMF and blog summarizer produces better relevant and summarized number of blogs compared to various search engines. SBMF confirms the hundred percentage relevancy compared to other blog search engines. Blog summarizer yields accurate summarization for the collected blogs.


© 2013 Godfrey Winster Sathianesan and Swamynathan Sankaranarayanan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.