TY - GEN
T1 - M-Cypher
T2 - 29th ACM International Conference on Information and Knowledge Management, CIKM 2020
AU - Li, Xiaodong
AU - Cheng, Reynold
AU - Najafi, Matin
AU - Chang, Kevin
AU - Han, Xiaolin
AU - Cao, Hongtai
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/10/19
Y1 - 2020/10/19
N2 - Graph databases witness the rise of Graph Query Language (GQL) in recent years, which enables non-programmers to express a graph query. However, the current solution does not support motif-related queries on knowledge graphs, which are proven important in many real-world scenarios. In this paper, we propose a GQL framework for mining knowledge graphs, named M-Cypher. It supports motif-related graph queries in an effective, efficient and user-friendly manner. We demonstrate the usage of the system by the emerging Covid-19 knowledge graph analytic tasks.
AB - Graph databases witness the rise of Graph Query Language (GQL) in recent years, which enables non-programmers to express a graph query. However, the current solution does not support motif-related queries on knowledge graphs, which are proven important in many real-world scenarios. In this paper, we propose a GQL framework for mining knowledge graphs, named M-Cypher. It supports motif-related graph queries in an effective, efficient and user-friendly manner. We demonstrate the usage of the system by the emerging Covid-19 knowledge graph analytic tasks.
KW - covid-19 knowledge graph
KW - gql
KW - motif
UR - https://www.scopus.com/pages/publications/85095864966
U2 - 10.1145/3340531.3417440
DO - 10.1145/3340531.3417440
M3 - Conference contribution
AN - SCOPUS:85095864966
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 3433
EP - 3436
BT - CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 19 October 2020 through 23 October 2020
ER -