AMiner: Toward Understanding Big Scholar Data

In this talk, I will present a novel academic search and mining system, AMiner, the second generation of the ArnetMiner system. Different from traditional academic search systems that focus on document (paper) search, AMiner aims to provide a systematic modeling approach for researchers (authors), ultimately to gain a deep understanding of the big (heterogeneous) network formed by authors, papers they have published, and venues they published those papers. The system extracts researchers' profiles automatically from the Web and integrates the researcher profiles with publication papers after name disambiguation. For now, the system has collected a big scholar data with more than 130,000,000 researcher profiles and 100,000,000 papers from multiple publication databases. We also developed an approach named COSNET to connect AMiner with several professional social networks such as LinkedIn and VideoLectures, which significantly enriches the metadata of the scholarly data. Based on the integrated big scholar data, we devise a unified topic modeling approach for modeling the different entities (authors, papers, venues) simultaneously and provide a topic-level expertise search by leveraging the modeling results. In addition, AMiner offers a set of researcher-centered functions including social influence analysis, influence visualization, collaboration recommendation, relationship mining, similarity analysis and community evolution. The system has been put into operation since 2006 and has attracted more than 7,000,000 independent IP accesses from over 200 countries/regions.