TY - CHAP

T1 - AGDISTIS - Graph-based disambiguation of named entities using linked data

AU - Usbeck, Ricardo

AU - Ngomo, Axel Cyrille Ngonga

AU - Röder, Michael

AU - Gerber, Daniel

AU - Coelho, Sandro Athaide

AU - Auer, Sören

AU - Both, Andreas

N1 - Conference code: 13

PY - 2014/1/1

Y1 - 2014/1/1

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 Web of Data 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. While several approaches aim to tackle this problem, they still achieve poor accuracy. We address this drawback 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. We evaluate our approach on eight different datasets against state-of-theart named entity disambiguation frameworks. Our results indicate that we outperform the state-of-the-art approach by up to 29% F-measure.

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 Web of Data 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. While several approaches aim to tackle this problem, they still achieve poor accuracy. We address this drawback 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. We evaluate our approach on eight different datasets against state-of-theart named entity disambiguation frameworks. Our results indicate that we outperform the state-of-the-art approach by up to 29% F-measure.

KW - Informatics

UR - http://www.scopus.com/inward/record.url?scp=84908688838&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/58a840bb-c51a-3ddb-abe3-ae7fb2518447/

U2 - 10.1007/978-3-319-11964-9_29

DO - 10.1007/978-3-319-11964-9_29

M3 - Article in conference proceedings

AN - SCOPUS:84908688838

SN - 978-3-319-11963-2

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 457

EP - 471

BT - The SemanticWeb - ISWC 2014 - 13th International SemanticWeb Conference, Proceedings

A2 - Tudorache, Tania

A2 - Knoblock, Craig

A2 - Groth, Paul

A2 - Goble, Carole

A2 - Welty, Chris

A2 - Bernstein, Abraham

A2 - Mika, Peter

A2 - Vrandečić, Denny

A2 - Noy, Natasha

A2 - Janowicz, Krzysztof

PB - Springer-Verlag Wien

T2 - 13th International Semantic Web Conference, ISWC 2014

Y2 - 19 October 2014 through 23 October 2014

ER -