M-Cypher: A GQL Framework Supporting Motifs

Xiaodong Li, Reynold Cheng, Matin Najafi, Kevin Chen-Chuan Chang, Xiaolin Han, Hongtai Cao

Abstract: 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.