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DTSTAMP:20260422T000711Z
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DTSTART;TZID=America/Denver:20231115T171500
DTEND;TZID=America/Denver:20231115T184500
UID:submissions.supercomputing.org_SC23_sess409_bof171@linklings.com
SUMMARY:Knowledge Graphs: How Will They Transform Science?
DESCRIPTION:Sagi Zisman (National Renewable Energy Laboratory (NREL)); Gre
 tchen Greene (National Institute of Standards and Technology (NIST)); Eric
  Stephan (Pacific Northwest National Laboratory (PNNL)); Marshall McDonnel
 l (Oak Ridge National Laboratory (ORNL)); Stuart Chalk (University of Nort
 h Florida); Ambarish Nag, Dmitry Duplyakin, Graham Johnson, David Rager, H
 arrison Goldwyn, and Nalinrat Guba (National Renewable Energy Laboratory (
 NREL)); and Gabe Fierro (Colorado School of Mines)\n\nDiverse big data, in
 terdisciplinary science, ML/AI applications and in-situ computations neces
 sitate knowledge representation. Knowledge, organized for machine understa
 nding in graph form known as knowledge graphs, augments large-scale scienc
 e. For example, biology and semantic web utilize large knowledge graphs. U
 tilizing AI, knowledge graphs enable natural language querying of linked i
 nformation, semantic recommendation systems, and knowledge completion. HPC
  challenges abound including parallelizing queries, retrieval-efficient kn
 owledge representation, and knowledge graph context-exploiting AI. This Bo
 F will introduce big ideas, as lightning talks followed by discussion, and
  engage a general audience in a discussion of emerging research topics aim
 ing to seed a community for collaboration.\n\nTag: Applications\n\nRegistr
 ation Category: Tech Program Reg Pass, Exhibits Reg Pass\n\n
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