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Article Information:
Measuring the Relevancy between Tags and Citation in Social Web
Saif Ur Rehman, Ghani-ur-Rehman, Aftab Ali Haider, Tanveer Afzal and Kamran Aziz
Corresponding Author: Saif Ur Rehman
Submitted: February 08, 2014
Accepted: April 09, 2014
Published: June 25, 2014 |
Abstract:
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With the advent of web, massive information is available to the internet users. One can acquire information from this according to his or her own field of interest; for example we can have large amount of information on bioinformatics available on the web, computer researcher community can found any type of published data at any period of time with just a single click on the Google or any other well renewed web search engines. Filtering the most relevant information from a large dump of online information is considered a challenging task, which is gaining popularity in the web research community. Now, various scientific tools and techniques have been introduced which enable the users to extract the relevant and required information. The accuracy of the information extracted is an interrogative mark. In research community the citation is very common term. Citations are used to extract the historic information relevant to some particular topic. But the citation of a specific research article requires enough time to cite a paper. However, in today’s social bookmarking period of the concept of tags is gaining popularity. Tags are assigned to papers or some topic by reviewers or its readers in a short period of time. In this study, we worked to find the relevance among the citations of a research paper to the tags assigned to these papers. Furthermore, we have obtained the titles of the cited papers and then perform comprehensive analysis on how much the tags are involved in titling the upcoming research articles. This will be helpful to argue that a tag can be used to assess the future diffusion of a research paper. For this we have provided our own framework. For validation of our framework, we have used the CiteULike, a very renewed social bookmarking web site articles to evaluate the performance of our proposed framework.
Key words: Citation, co-relation, knowledge discovery, social bookmarking, statistical analysis, web,
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Cite this Reference:
Saif Ur Rehman, Ghani-ur-Rehman, Aftab Ali Haider, Tanveer Afzal and Kamran Aziz, . Measuring the Relevancy between Tags and Citation in Social Web. Research Journal of Applied Sciences, Engineering and Technology, (24): 5172-5178.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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