Article ranking with citation context analysis

It is hard to detect important articles in a specific context. Information retrieval techniques based on full text search can be inaccurate to identify main topics and they are not able to provide an indication about the importance of the article. Generating a citation network is a good way to find most popular articles but this approach is not context aware. The text around a citation mark is generally a good summary of referred article. So citation context analysis presents an opportunity to use the wisdom of crowd for detecting important articles in a context sensitive way. In this work, we analyze citation contexts to rank articles properly for a given topic. The model proposed here uses citation contexts in order to create a directed and weighted citation network based on the target topic. We create a directed and weighted edge between two articles if citation context contains terms from the term set we created for the target topic. Then we apply common ranking algorithms for the vertices of network. We showed that this method successfully detects the most prominent articles in a given topic. The biggest contribution of this approach is that we are able to identify important articles in the target topic even though they don't contain the term represents the interested context.