AGDISTIS-agnostic disambiguation of named entities using linked open data
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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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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TY - CHAP
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.
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=84923171874&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/13e936b1-69cb-3b25-bb69-dbe3af351a69/
U2 - 10.3233/978-1-61499-419-0-1113
DO - 10.3233/978-1-61499-419-0-1113
M3 - Article in conference proceedings
AN - SCOPUS:84923171874
SN - 978-1-61499-418-3
T3 - Frontiers in Artificial Intelligence and Applications
SP - 1113
EP - 1114
BT - ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings
A2 - Schaub, Torsten
A2 - Friedrich, Gerhard
A2 - O'Sullivan, Barry
PB - IOS Press BV
T2 - 21st European Conference on Artificial Intelligence, ECAI 2014
Y2 - 18 August 2014 through 22 August 2014
ER -