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AGDISTIS - agnostic disambiguation of named entities using linked open data

: Usbeck, R.; Ngonga Ngomo, A.-C.; Röder, M.; Gerber, D.; Athaide Coelho, S.; Auer, S.; Both, A.


Schaub, T. ; European Coordinating Committee on Artificial Intelligence -ECCAI-:
ECAI 2014, 21st European Conference on Artificial Intelligence. Proceedings : 18-22 August 2014, Prague, Czech Republic, including Conference on Prestigious Applications of Intelligent Systems (PAIS 2014)
Amsterdam: IOS Press, 2014 (Frontiers in Artificial Intelligence and Applications 263)
ISBN: 978-1-61499-418-3 (Print)
ISBN: 978-1-61499-419-0 (Online)
European Conference on Artificial Intelligence (ECAI) <21, 2014, Prague>
Conference on Prestigious Applications of Intelligent Systems (PAIS) <2014, Prague>
Fraunhofer IAIS ()

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.