Mining Landmark Papers

Abstract

In recent years, the number of electronic journal articles is growing faster than ever before; information is generated faster than people can deal with it. In order to handle this problem, many electronic periodical databases have proposed keyword search methods to decrease the effort and time spent by users in searching the journal's archives. However, the users still have to deal with a huge number of search results. In this paper, we present the problem of mining landmark papers. We treat papers that introduce important keyphrases for the first time as landmark papers. Our approach combines simple ideas from text mining, information extraction and information retrieval to identify landmark papers. We show that existing related techniques such as first story detection, mining hot topics and theme mining do not effectively handle the landmark paper mining problem. Our approach is simpler and more direct for this task. We experimentally evaluate our approach on a large dataset of papers in the database or data mining areas downloaded using DBLP.