Semantic Web Improved with Fuzziness added in Weighted Score
A lot of improvement has gone in the area of information retrieval. But, still improvements can be done. Social networking giants like Facebook, LinkedIn, CiteULike have taken a new role. There is a huge data collection from these sites. A lot of work is going on to convert this data into information. As we are aware that term weighting has a significant role in text classification. Many techniques of text classification are based on the term frequency (tf) and inverse document frequency (idf) for representing importance of terms and computing weights in classifying a text document. In this paper, we are extending the queries by “keyword+tags” instead of keywords only. In addition to this, we have developed a new ranking algorithm which utilizes semantic tags to enhance the already existing semantic web by using the weighted score. The data for the tags has been obtained through CiteUlike. Here, we have manually added fuzziness in the weighted score for the purpose of improving the algorithm.
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