This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.
%0 Conference Paper
%1 citeulike:5909891
%A Tang, Jie
%A Zhang, Jing
%A Yao, Limin
%A Li, Juanzi
%A Zhang, Li
%A Su, Zhong
%B Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
%C New York, NY, USA
%D 2008
%I ACM
%K academic-reference social-network
%P 990--998
%R 10.1145/1401890.1402008
%T ArnetMiner: Extraction and Mining of Academic Social Networks
%U http://dx.doi.org/10.1145/1401890.1402008
%X This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.
%@ 978-1-60558-193-4
@inproceedings{citeulike:5909891,
abstract = {{This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.}},
added-at = {2018-03-19T12:24:51.000+0100},
address = {New York, NY, USA},
author = {Tang, Jie and Zhang, Jing and Yao, Limin and Li, Juanzi and Zhang, Li and Su, Zhong},
biburl = {https://www.bibsonomy.org/bibtex/2f383f1fb34f37cb15c694a47ce0973b8/aho},
booktitle = {Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
citeulike-article-id = {5909891},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1402008},
citeulike-linkout-1 = {http://dx.doi.org/10.1145/1401890.1402008},
doi = {10.1145/1401890.1402008},
interhash = {4bc1545b7409faef5592da449a2d56f7},
intrahash = {f383f1fb34f37cb15c694a47ce0973b8},
isbn = {978-1-60558-193-4},
keywords = {academic-reference social-network},
location = {Las Vegas, Nevada, USA},
pages = {990--998},
posted-at = {2015-11-17 02:50:05},
priority = {2},
publisher = {ACM},
series = {KDD '08},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{ArnetMiner: Extraction and Mining of Academic Social Networks}},
url = {http://dx.doi.org/10.1145/1401890.1402008},
year = 2008
}