Conference paper Open Access

Mining and Leveraging Background Knowledge for Improving Named Entity Linking

Weichselbraun, Albert; Kuntschik, Philipp; Braşoveanu, Adrian M. P.


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://zenodo.org/record/2534576</identifier>
  <creators>
    <creator>
      <creatorName>Weichselbraun, Albert</creatorName>
      <givenName>Albert</givenName>
      <familyName>Weichselbraun</familyName>
      <affiliation>Swiss Institute for Information Research - University of Applied Sciences Chur Chur, Switzerland</affiliation>
    </creator>
    <creator>
      <creatorName>Kuntschik, Philipp</creatorName>
      <givenName>Philipp</givenName>
      <familyName>Kuntschik</familyName>
      <affiliation>Swiss Institute for Information Research - University of Applied Sciences Chur Chur, Switzerland</affiliation>
    </creator>
    <creator>
      <creatorName>Braşoveanu, Adrian M. P.</creatorName>
      <givenName>Adrian M. P.</givenName>
      <familyName>Braşoveanu</familyName>
      <affiliation>Swiss Institute for Information Research - University of Applied Sciences Chur Chur, Switzerland</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Mining and Leveraging Background Knowledge for Improving Named Entity Linking</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>Knowledge-rich Information Extraction</subject>
    <subject>Named Entity Linking</subject>
    <subject>Linked Data Quality</subject>
    <subject>Information Extraction</subject>
    <subject>Semantic Technologies</subject>
    <subject>Natural Language Processing</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-06-27</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2534576</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3227609.3227670</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/invid-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Knowledge-rich Information Extraction (IE) methods aspire towards combining classical IE with background knowledge obtained from third-party resources. Linked Open Data repositories that encode billions of machine readable facts from sources such as Wikipedia play a pivotal role in this development. The recent growth of Linked Data adoption for Information Extraction tasks has shed light on many data quality issues in these data sources that seriously challenge their usefulness such as completeness, timeliness and semantic correctness. Information Extraction methods are, therefore, faced with problems such as name variance and type confusability. If multiple linked data sources are used in parallel, additional concerns regarding link stability and entity mappings emerge. This paper develops methods for integrating Linked Data into Named Entity Linking methods and addresses challenges in regard to mining knowledge from Linked Data, mitigating data quality issues, and adapting algorithms to leverage this knowledge. Finally, we apply these methods to Recognyze, a graph-based Named Entity Linking (NEL) system, and provide a comprehensive evaluation which compares its performance to other well-known NEL systems, demonstrating the impact of the suggested methods on its own entity linking performance.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/687786/">687786</awardNumber>
      <awardTitle>In Video Veritas – Verification of Social Media Video Content for the News Industry</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
18
25
views
downloads
Views 18
Downloads 25
Data volume 18.6 MB
Unique views 16
Unique downloads 22

Share

Cite as