Presentation Open Access

Automated Metadata Extraction: Challenges and Opportunities

Tyler Skluzacek; Kyle Chard; Ian Foster

DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="" xmlns="" xsi:schemaLocation="">
  <identifier identifierType="DOI">10.5281/zenodo.7182583</identifier>
      <creatorName>Tyler Skluzacek</creatorName>
      <affiliation>Oak Ridge National Lab</affiliation>
      <creatorName>Kyle Chard</creatorName>
      <affiliation>University of Chicago and Argonne National Lab</affiliation>
      <creatorName>Ian Foster</creatorName>
      <affiliation>University of Chicago and Argonne National Lab</affiliation>
    <title>Automated Metadata Extraction: Challenges and Opportunities</title>
    <subject>data mining</subject>
    <date dateType="Issued">2022-10-10</date>
  <resourceType resourceTypeGeneral="Text">Presentation</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.7182582</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf"></relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Proper application of the FAIR data principles is what separates a vibrant data ecosystem, in which research data are frequently shared and reused, from a lifeless data graveyard. Automated metadata extraction systems have been proposed as a means of bolstering the findability, interoperability, and reusabil- ity of data repositories with little or no human intervention. These extraction systems mine metadata by crawling a repository and applying lightweight extractors that, for various types of file (e.g., image, CSV file), extract or synthesize relevant attributes. In practice, however, the automated creation of generally useful metadata is fraught with challenges. Data consumers may have different perspectives as to what metadata representations are useful, the standards for recording metadata tend to change over time, and the software model for processing updates can introduce unnecessary human and computational effort. Thus, generalizing extraction for a broad audience of data consumers is a difficult and relatively unsolved problem.&lt;/p&gt;

&lt;p&gt;In this work, we explore these challenges faced by extraction systems in the context of constructing our own extraction system for science data. We first define the metadata extraction problem and provide context to the issues faced in generalizing metadata. Additionally, we identify potential research directions to help alleviate many of these challenges for all automated extraction systems. Ultimately, this work represents a first step in designing ubiquitous metadata extraction systems that can maximize the value of research data while minimizing the human efforts required in doing so.&lt;/p&gt;</description>
All versions This version
Views 1010
Downloads 1616
Data volume 67.8 MB67.8 MB
Unique views 1010
Unique downloads 1515


Cite as