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[ "张秋颖(1995-),女,上海交通大学硕士生,主要研究方向为学术大数据和网络爬虫。" ]
[ "周乐(1998-),男,上海交通大学在读,主要研究方向为学术大数据和数据可视化。" ]
[ "唐静瑶(1999-),女,上海交通大学在读,主要研究方向为学术大数据和数据可视化。" ]
[ "傅洛伊(1987-),女,上海交通大学副教授,主要研究方向为社交网络与大数据。" ]
[ "王新兵(1975-),男,上海交通大学特聘教授,主要研究方向为移动互联网、物联网和大数据。" ]
纸质出版日期:2018-12-30,
网络出版日期:2018-12,
移动端阅览
张秋颖, 周乐, 唐静瑶, 等. 万物互联:学术数据的互联、挖掘与可视化[J]. 物联网学报, 2018,2(4):56-60.
QIUYING ZHANG, LE ZHOU, JINGYAO TANG, et al. Internet of everything:interconnection,mining and visualization of academic data. [J]. Chinese journal on internet of things, 2018, 2(4): 56-60.
张秋颖, 周乐, 唐静瑶, 等. 万物互联:学术数据的互联、挖掘与可视化[J]. 物联网学报, 2018,2(4):56-60. DOI: 10.11959/j.issn.2096-3750.2018.00074.
QIUYING ZHANG, LE ZHOU, JINGYAO TANG, et al. Internet of everything:interconnection,mining and visualization of academic data. [J]. Chinese journal on internet of things, 2018, 2(4): 56-60. DOI: 10.11959/j.issn.2096-3750.2018.00074.
随着物联网的不断发展,“物”的概念已扩展至学术数据领域。由于物联网节点的海量性以及节点关系的复杂性,用户很难直接从互联的学术数据中获得对所需信息的进一步分析。AceMap 作为一个学术搜索系统,为了能够帮助用户获得全方位的学术信息,通过自主研发的 AceKG 学术知识图谱,为用户提供了个性化查询以及实时生成结果的服务;同时,以学术地图(如论文地图、学者地图等)的方式直观呈现学术数据之间的关系,帮助用户高效获取所需信息。
With the continuous development of the Internet of things
the concept of “things” has also expanded to academic data.Due to the massiveness of IoT node and the complexity of node relationships
it is difficult for users to obtain further analysis of the required information directly from these interconnected academic data.As an academic search system
in order to help users obtain comprehensive academic information
personalized inquiry and real-time results generation services were provided to users by AceMap through the self-developed AceKG academic knowledge map.At the same time
AceMap presents the relationship between academic data visually in the form of academic maps (such as paper maps
author maps
etc.)
and users are helped to get the information they need efficiently.
物联网学术大数据可视化知识图谱
Internet of thingsacademic big datavisualizationknowledge map
WANG R, YAN Y, WANG J ,et al. AceKG:a large-scale knowledge graph for academic data mining[C]// Proceedings of the 27th ACM International Conference on Information and Knowledge Management,October 22-26,2018,Torino,Italy. New York:ACM, 2018: 1487-1490.
SINGHAL A . Introducing the knowledge graph:things,not strings[J]. Official Google Blog, 2012
HOFFART J, SUCHANEK F M, BERBERICH K ,et al. Yago2:a spatially and temporally enhanced knowledge base from Wikipedia[J]. Artificial Intelligence, 2013,194: 28-61.
MITCHELLT M, COHENW W, HRUSCHKAE R ,et al. Never-ending learning[C]// Twenty-ninth AAAI Conference on Artificial Intelligence,January 25-30,2015,Austin,Texas. Palo Alto:AAAI Press, 2015.
JENS L, ROBERT I, MAX J ,et al. Dbpedia——a largescale,multilingual knowledge base extracted from Wikipedia[J]. Semantic Web Journal, 2015,6(2): 167-195.
CHRISTOPHER D S, ALEX R, CHRISTOPHER R ,et al. Deepdive:declarative knowledge base construction[J]. Sigmod Record, 2016,45(1): 60-67.
HU Y . Efficient,high-quality force-directed graph drawing[J]. Mathematica Journal, 1984,10(1): 37-71.
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).
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