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It is hard to detect important articles in a specific context. Informationretrieval techniques based on full text search can be inaccurate to identifymain topics and they are not able to provide an indication about the importanceof the article. Generating a citation network is a good way to find mostpopular articles but this approach is not context aware.  The text around a citation mark is generally a good summary of the referredarticle. So citation context analysis presents an opportunity to use the wisdomof crowd for detecting important articles in a context sensitive way. In thiswork, we analyze citation contexts to rank articles properly for a given topic.The model proposed uses citation contexts in order to create a directed andweighted citation network based on the target topic. We create a directed andweighted edge between two articles if citation context contains terms relatedwith the target topic. Then we apply common ranking algorithms in order to findimportant articles in this newly created network. We showed that this methodsuccessfully detects a good subset of most prominent articles in a given topic.The biggest contribution of this approach is that we are able to identifyimportant articles for a given search term even though these articles do notcontain this search term. This technique can be used in other linked documentsincluding web pages, legal documents, and patents.

Context Sensitive Article Ranking with Citation Context Analysis