Academic big data can provide help on information to researchers. While traditional academic search engines only offer search results without analysis, academic map is proposed based on AceMap, visualizing academic information to users in form of graph with various types of designed maps. When SQL-based database is not capable for generating customized academic map, AceKG is proposed to store data in form of triplets. AceKG can help realize the heterogeneous and customized map generator and can generate correlation map for any type of entity after embedding. Through the visualization of academic data, users are able to find points of interest more intuitively, thus improves the practicality of academic platform.
ZHANG Ye,JIA Yuting,FU Luoyi,WANG Xinbing
. AceMap Academic Map and AceKG Academic Knowledge Graph for
Academic Data Visualization[J]. Journal of Shanghai Jiaotong University, 2018
, 52(10)
: 1357
-1362
.
DOI: 10.16183/j.cnki.jsjtu.2018.10.026
[1]TAN Z, LIU C, MAO Y, et al. AceMap: A novel approach towards displaying relationship among academic literatures[C]//International Conference Companion on World Wide Web. Montreal: 25th International World Wide Web Conferences Steering Committee, 2016: 437-442.
[2]HU Y. Efficient, high-quality force-directed graph drawing[J]. Mathematica Journal, 1984, 10(1): 37-71.
[3]JACOMY M, BASTIAN M, HEYMANN S. Gephi: An open source software for exploring and manipulating networks[C]//International AAAI Conference on Weblogs and Social Media. San Jose: 3rd International AAAI Conference on Weblogs and Social Media, 2009.
[4]JACOMY M, VENTURINI T, HEYMANN S, et al. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the gephi software[J]. Plos One, 2014, 9(6): e98679.
[5]BLONDEL V D, GUILLAUME J L, LAMBIOTTE R, et al. Fast unfolding of communities in large networks[J]. Journal of Statistical Mechanics, 2008, 2008(10): 155-168.
[6]YANG B, YIH W T, HE X, et al. Embedding entities and relations for learning and inference in knowledge bases[C]//International Conference on Learning Representations. San Diego: 3rd International Conference on Learning Representations, 2015.
[7]JOLLIFFE I T. Principal component analysis [M]. Berlin: Springer, 1986: 115-128.