TY - GEN
T1 - Visualizing and curating knowledge graphs over time and space
AU - Ge, Tong
AU - Wang, Yafang
AU - De Melo, Gerard
AU - Li, Haofeng
AU - Chen, Baoquan
N1 - Funding Information:
This project was sponsored by National 973 Program (No. 2015CB352500), National Natural Science Foundation of China (No. 61503217), Shandong Provincial Natural Science Foundation of China (No. ZR2014FP002), and The Fundamental Research Funds of Shandong University (No. 2014T-B005, 2014JC001). Gerard de Melo's research is supported by China 973 Program Grants 2011C-BA00300, 2011CBA00301, and NSFC Grants 61033001, 61361136003, 61550110504.
Publisher Copyright:
© 2016 Association for Computational Linguistics.
PY - 2016
Y1 - 2016
N2 - Publicly available knowledge repositories, such as Wikipedia and Freebase, benefit significantly from volunteers, whose contributions ensure that the knowledge keeps expanding and is kept up-to-date and accurate. User interactions are often limited to hypertext, tabular, or graph visualization interfaces. For spatio-temporal information, however, other interaction paradigms may be better-suited. We present an integrated system that combines crowdsourcing, automatic or semi-automatic knowledge harvesting from text, and visual analytics. It enables users to analyze large quantities of structured data and unstructured textual data from a spatio-temporal perspective and gain deep insights that are not easily observed in individual facts.
AB - Publicly available knowledge repositories, such as Wikipedia and Freebase, benefit significantly from volunteers, whose contributions ensure that the knowledge keeps expanding and is kept up-to-date and accurate. User interactions are often limited to hypertext, tabular, or graph visualization interfaces. For spatio-temporal information, however, other interaction paradigms may be better-suited. We present an integrated system that combines crowdsourcing, automatic or semi-automatic knowledge harvesting from text, and visual analytics. It enables users to analyze large quantities of structured data and unstructured textual data from a spatio-temporal perspective and gain deep insights that are not easily observed in individual facts.
UR - https://www.scopus.com/pages/publications/85016486947
UR - https://www.scopus.com/pages/publications/85016486947#tab=citedBy
U2 - 10.18653/v1/p16-4005
DO - 10.18653/v1/p16-4005
M3 - Conference contribution
AN - SCOPUS:85016486947
T3 - 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - System Demonstrations
SP - 25
EP - 30
BT - 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - System Demonstrations
PB - Association for Computational Linguistics (ACL)
T2 - 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016
Y2 - 7 August 2016 through 12 August 2016
ER -