M-Cypher: A GQL Framework Supporting Motifs

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages3433-3436
Number of pages4
ISBN (Electronic)9781450368599
DOIs
StatePublished - Oct 19 2020
Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
Duration: Oct 19 2020Oct 23 2020

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
CountryIreland
CityVirtual, Online
Period10/19/2010/23/20

Keywords

  • covid-19 knowledge graph
  • gql
  • motif

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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