AMiner: Mining Deep Knowledge from Big Scholar Data

AMiner(1) is the second generation of the ArnetMiner system [6]. We focus on developing author-centric analytic and mining tools for gaining a deep understanding of the large and heterogeneous networks formed by authors, papers, venues, and knowledge concepts. One fundamental goal is how to extract and integrate semantics from different sources. We have developed algorithms to automatically extract researchers' profiles from the Web [5] and resolve the name ambiguity problem [3], and connect different professional networks [9]. We also developed methodologies to incorporate knowledge from the Wikipedia and other sources into the system [7, 2] to bridge the gap between network science and the web mining research. In this talk, I will focus on answering two fundamental questions for author-centric network analysis: who is who? and who are similar to each other? The system has been in operation since 2006 and has collected more than 100,000,000 author profiles, 100,000,000 publication papers, and 7,800,000 knowledge concepts. It has been widely used for collaboration recommendation [4], similarity analysis [8], and community evolution [1].