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2016 IEEE 32nd International Conference on Data Engineering (ICDE) (2016)
Helsinki, Finland
May 16, 2016 to May 20, 2016
ISBN: 978-1-5090-2020-1
pp: 1350-1353
Jun Chen , School of Information, Renmin University of China, Beijing, China
Yueguo Chen , School of Information, Renmin University of China, Beijing, China
Xiaoyong Du , School of Information, Renmin University of China, Beijing, China
Xiangling Zhang , School of Information, Renmin University of China, Beijing, China
Xuan Zhou , School of Information, Renmin University of China, Beijing, China
ABSTRACT
Large-scale knowledge graphs (KGs) contain massive entities and abundant relations among the entities. Data exploration over KGs allows users to browse the attributes of entities as well as the relations among entities. It therefore provides a good way of learning the structure and coverage of KGs. In this paper, we introduce a system called SEED that is designed to support entity-oriented exploration in large-scale KGs, based on retrieving similar entities of some seed entities as well as their semantic relations that show how entities are similar to each other. A by-product of entity exploration in SEED is to facilitate discovering the deficiency of KGs, so that the detected bugs can be easily fixed by users as they explore the KGs.
INDEX TERMS
Debugging, Semantics, Visualization, Generators, Knowledge engineering, User interfaces, Itemsets
CITATION
Jun Chen, Yueguo Chen, Xiaoyong Du, Xiangling Zhang, Xuan Zhou, "SEED: A system for entity exploration and debugging in large-scale knowledge graphs", 2016 IEEE 32nd International Conference on Data Engineering (ICDE), vol. 00, no. , pp. 1350-1353, 2016, doi:10.1109/ICDE.2016.7498342
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