AGDISTIS-agnostic disambiguation of named entities using linked open data

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

doi:10.3233/978-1-61499-419-0-1113

AGDISTIS-agnostic disambiguation of named entities using linked open data

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

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

19 Citations (Scopus)

Abstract

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 language	English
Title of host publication	ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings
Editors	Torsten Schaub, Gerhard Friedrich, Barry O'Sullivan
Number of pages	2
Publisher	IOS Press BV
Publication date	2014
Pages	1113-1114
ISBN (Print)	978-1-61499-418-3
ISBN (Electronic)	978-1-61499-419-0
DOIs	https://doi.org/10.3233/978-1-61499-419-0-1113
Publication status	Published - 2014
Externally published	Yes
Event	21st European Conference on Artificial Intelligence, ECAI 2014 - Prague, Czech Republic Duration: 18.08.2014 → 22.08.2014 Conference number: 21 http://www.ecai2014.org/

Bibliographical note

Publisher Copyright:
© 2014 The Authors and IOS Press.

Research areas and keywords

Informatics
Business informatics

Access to Document

10.3233/978-1-61499-419-0-1113Licence: CC BY-NC

Cite this

Usbeck, R., Ngonga Ngomo, A. C., Röder, M., Gerber, D., Athaide Coelho, S., Auer, S., & Both, A. (2014). AGDISTIS-agnostic disambiguation of named entities using linked open data. In T. Schaub, G. Friedrich, & B. O'Sullivan (Eds.), ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings (pp. 1113-1114). (Frontiers in Artificial Intelligence and Applications; Vol. 263). IOS Press BV. https://doi.org/10.3233/978-1-61499-419-0-1113

Usbeck, Ricardo ; Ngonga Ngomo, Axel Cyrille ; Röder, Michael et al. / AGDISTIS-agnostic disambiguation of named entities using linked open data. ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings. editor / Torsten Schaub ; Gerhard Friedrich ; Barry O'Sullivan. IOS Press BV, 2014. pp. 1113-1114 (Frontiers in Artificial Intelligence and Applications).

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abstract = "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.",

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Usbeck, R, Ngonga Ngomo, AC, Röder, M, Gerber, D, Athaide Coelho, S, Auer, S & Both, A 2014, AGDISTIS-agnostic disambiguation of named entities using linked open data. in T Schaub, G Friedrich & B O'Sullivan (eds), ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings. Frontiers in Artificial Intelligence and Applications, vol. 263, IOS Press BV, pp. 1113-1114, 21st European Conference on Artificial Intelligence, ECAI 2014, Prague, Czech Republic, 18.08.14. https://doi.org/10.3233/978-1-61499-419-0-1113

AGDISTIS-agnostic disambiguation of named entities using linked open data. / Usbeck, Ricardo; Ngonga Ngomo, Axel Cyrille; Röder, Michael et al.
ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings. ed. / Torsten Schaub; Gerhard Friedrich; Barry O'Sullivan. IOS Press BV, 2014. p. 1113-1114 (Frontiers in Artificial Intelligence and Applications; Vol. 263).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

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T1 - AGDISTIS-agnostic disambiguation of named entities using linked open data

AU - Usbeck, Ricardo

AU - Ngonga Ngomo, Axel Cyrille

AU - Röder, Michael

AU - Gerber, Daniel

AU - Athaide Coelho, Sandro

AU - Auer, Sören

AU - Both, Andreas

N1 - Conference code: 21

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

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SN - 978-1-61499-418-3

T3 - Frontiers in Artificial Intelligence and Applications

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EP - 1114

BT - ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings

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PB - IOS Press BV

T2 - 21st European Conference on Artificial Intelligence, ECAI 2014

Y2 - 18 August 2014 through 22 August 2014

ER -

Usbeck R, Ngonga Ngomo AC, Röder M, Gerber D, Athaide Coelho S, Auer S et al. AGDISTIS-agnostic disambiguation of named entities using linked open data. In Schaub T, Friedrich G, O'Sullivan B, editors, ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings. IOS Press BV. 2014. p. 1113-1114. (Frontiers in Artificial Intelligence and Applications). doi: 10.3233/978-1-61499-419-0-1113

AGDISTIS-agnostic disambiguation of named entities using linked open data

Abstract

Bibliographical note

Research areas and keywords

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Cite this