Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: AMiner is the second generation of the ArnetMiner system.  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.  have developed algorithms to automatically extract researchers' profiles from the b and re- solve the name ambiguity problem, and connect different professional networks.  also developed methodologies to incorporate knowledge from the Wikipedia and other sources into the system to bridge the gap beten network science and the b mining research. In this talk, I will focus on ansring 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, similarity analysis, and community evolution.
['Jie Tang']
I am writing a research paper on a idea AMiner: Mining Deep Knowledge from Big Scholar Data
[{"from": "human", "value": "I am writing a research paper on a idea AMiner: Mining Deep Knowledge from Big Scholar Data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: AMiner is the second generation of the ArnetMiner system.  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.  have developed algorithms to automatically extract researchers' profiles from the b and re- solve the name ambiguity problem, and connect different professional networks.  also developed methodologies to incorporate knowledge from the Wikipedia and other sources into the system to bridge the gap beten network science and the b mining research. In this talk, I will focus on ansring 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, similarity analysis, and community evolution."}]