PropertyValue
?: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 © 2020 ACM
is ?:annotates of
?:creator
?:journal
  • 29th_ACM_International_Conference_on_Information_and_Knowledge_Management,_CIKM_2020
?:license
  • unk
?:publication_isRelatedTo_Disease
?:source
  • WHO
?:title
  • M-Cypher: A GQL Framework Supporting Motifs
?:type
?:who_covidence_id
  • #926716
?:year
  • 2020

Metadata

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<http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2004/03/trix/rdfg-1/Graph>
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<http://www.ontologydesignpatterns.org/cp/owl/informationrealization.owl#realizes> <https://research.tib.eu/covid-19/data/entity/ds7t6t5e>
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