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BEGIN:VEVENT
DTSTAMP:20210402T160544Z
LOCATION:Poster Module
DTSTART;TZID=America/New_York:20201119T083000
DTEND;TZID=America/New_York:20201119T170000
UID:submissions.supercomputing.org_SC20_sess337_rpost158@linklings.com
SUMMARY:Semantic Search for Self-Describing Scientific Data Formats
DESCRIPTION:Posters, Research Posters\n\nSemantic Search for Self-Describi
 ng Scientific Data Formats\n\nNiu, Zhang, Byna, Chen\n\nIt is often a daun
 ting and challenging task for scientists to find datasets relevant to thei
 r needs. This is especially true for self-describing file formats, which a
 re often used for data storage in scientific applications. Existing soluti
 ons extract the metadata and process search queries with matching search k
 eywords in metadata via exact or partial lexical match approaches. They ar
 e hindered, however, by an inability to capture the semantic meaning of th
 e content of the metadata, and are therefore precluded from performing que
 ries at the semantic level. We propose a novel semantic search solution fo
 r self-describing datasets, which captures the semantic meaning of dataset
  metadata and achieves search functionality at semantic level. We have eva
 luated our approach and compared it against the existing solutions. Our ap
 proach demonstrates efficient semantic search performance.\n\nRegistration
  Category: Tech Program Reg Pass, Exhibits Reg Pass
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