AGDISTIS-agnostic disambiguation of named entities using linked open data

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

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.

Original languageEnglish
Title of host publicationECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings
EditorsTorsten Schaub, Gerhard Friedrich, Barry O'Sullivan
Number of pages2
PublisherIOS Press BV
Publication date2014
Pages1113-1114
ISBN (print)978-1-61499-418-3
ISBN (electronic)978-1-61499-419-0
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event21st European Conference on Artificial Intelligence, ECAI 2014 - Prague, Czech Republic
Duration: 18.08.201422.08.2014
Conference number: 21
http://www.ecai2014.org/

Bibliographical note

Publisher Copyright:
© 2014 The Authors and IOS Press.

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