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DTSTAMP:20211207T055407Z
LOCATION:Online
DTSTART;TZID=America/Chicago:20211115T165000
DTEND;TZID=America/Chicago:20211115T171500
UID:submissions.supercomputing.org_SC21_sess423_ws_mlhpce108@linklings.com
SUMMARY:HPC Ontology: Toward a Unified Ontology for Managing Training Data
 sets and AI Models for High-Performance Computing
DESCRIPTION:Workshop\n\nHPC Ontology: Toward a Unified Ontology for Managi
 ng Training Datasets and AI Models for High-Performance Computing\n\nLiao,
  Lin, Verma, Vanderbruggen, Emani...\n\nMachine learning (ML) techniques h
 ave been widely studied to address various challenges of productively and 
 efficiently running large-scale scientific applications on heterogeneous s
 upercomputers. However, it is extremely difficult to generate, access, and
  maintain training datasets and AI models to accelerate ML-based research.
   The  Future of Research Communications and e-Scholarship has proposed th
 e FAIR data  principles describing Findability, Accessibility, Interoperab
 ility, and  Reusability.  In this paper, we present our ongoing work of de
 signing an ontology for high-performance computing (named HPC ontology) in
  order to make training datasets and AI models FAIR. Our ontology provides
  controlled vocabularies, explicit semantics, and formal knowledge represe
 ntations.  Our design uses an extensible two-level pattern, capturing both
  high-level meta information and low-level data content for software, hard
 ware, experiments, workflows, training datasets, AI models, and so on. Pre
 liminary evaluation shows that HPC ontology is effective to annotate selec
 ted data and support a set of SPARQL queries.\n\nTag: Online Only, Machine
  Learning and Artificial Intelligence\n\nRegistration Category: Workshop R
 eg Pass
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