AGDISTIS-agnostic disambiguation of named entities using linked open data

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

  • Ricardo Usbeck
  • Axel Cyrille Ngonga Ngomo
  • Michael Röder
  • Daniel Gerber
  • Sandro Athaide Coelho
  • Sören Auer
  • Andreas Both

Over the last decades, several billion Web pages have been made available on the Web. The ongoing transition from the current Web of unstructured data to the Data Web yet requires scalable and accurate approaches for the extraction of structured data in RDF (Resource Description Framework) from these websites. One of the key steps towards extracting RDF from text is the disambiguation of named entities. We address this issue by presenting AGDISTIS, a novel knowledge-base-agnostic approach for named entity disambiguation. Our approach combines the Hypertext-Induced Topic Search (HITS) algorithm with label expansion strategies and string similarity measures. Based on this combination, AGDISTIS can efficiently detect the correct URIs for a given set of named entities within an input text.

OriginalspracheEnglisch
TitelECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings
HerausgeberTorsten Schaub, Gerhard Friedrich, Barry O'Sullivan
Anzahl der Seiten2
VerlagIOS Press BV
Erscheinungsdatum2014
Seiten1113-1114
ISBN (Print)978-1-61499-418-3
ISBN (elektronisch)978-1-61499-419-0
DOIs
PublikationsstatusErschienen - 2014
Extern publiziertJa
Veranstaltung21st European Conference on Artificial Intelligence, ECAI 2014 - Prague, Tschechische Republik
Dauer: 18.08.201422.08.2014
Konferenznummer: 21
http://www.ecai2014.org/

Bibliographische Notiz

Publisher Copyright:
© 2014 The Authors and IOS Press.

DOI

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