  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 the 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 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 related
with the target topic. Then we apply common ranking algorithms in order to find
important articles in this newly created network. We showed that this method
successfully detects a good subset of most prominent articles in a given topic.
The biggest contribution of this approach is that we are able to identify
important articles for a given search term even though these articles do not
contain this search term. This technique can be used in other linked documents
including web pages, legal documents, and patents.
