Navigation through Citation Network Based on Content Similarity Using Cosine Similarity Algorithm

The rate of scientific literature has been increased in the past few decades; new topics and information is added in the form of articles, papers, text documents, web logs, and patents. The growth of information at rapid rate caused a tremendous amount of additions in the current and past knowledge, during this process, new topics emerged, some topics split into many other sub-topics, on the other hand, many topics merge to formed single topic. The selection and search of a topic manually in such a huge amount of information have been found as an expensive and workforce-intensive task. For the emerging need of an automatic process to locate, organize, connect, and make associations among these sources the researchers have proposed different techniques that automatically extract components of the information presented in various formats and organize or structure them. The targeted data which is going to be processed for component extraction might be in the form of text, video or audio. The addition of different algorithms has structured information and grouped similar information into clusters and on the basis of their importance, weighted them. The organized, structured and weighted data is then compared with other structures to find similarity with the use of various algorithms. The semantic patterns can be found by employing visualization techniques that show similarity or relation between topics over time or related to a specific event. In this paper, we have proposed a model based on Cosine Similarity Algorithm for citation network which will answer the questions like, how to connect documents with the help of citation and content similarity and how to visualize and navigate through the document.