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

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

doi:10.1007/978-3-319-11964-9_29

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

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

^*Corresponding author for this work

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

132 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 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.

Original language	English
Title of host publication	The SemanticWeb - ISWC 2014 - 13th International SemanticWeb Conference, Proceedings
Editors	Tania Tudorache, Craig Knoblock, Paul Groth, Carole Goble, Chris Welty, Abraham Bernstein, Peter Mika, Denny Vrandečić, Natasha Noy, Krzysztof Janowicz
Number of pages	15
Publisher	Springer-Verlag Wien
Publication date	01.01.2014
Pages	457-471
ISBN (Print)	978-3-319-11963-2
ISBN (Electronic)	978-3-319-11964-9
DOIs	https://doi.org/10.1007/978-3-319-11964-9_29
Publication status	Published - 01.01.2014
Externally published	Yes
Event	13th International Semantic Web Conference, ISWC 2014 - Riva del Garda, Italy Duration: 19.10.2014 → 23.10.2014 Conference number: 13 https://search.worldcat.org/de/title/semantic-web-iswc-2014-13th-international-semantic-web-conference-riva-del-garda-italy-october-19-23-2014-proceedings-part-i/oclc/941304230

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2014.

Research areas and keywords

Informatics

Access to Document

10.1007/978-3-319-11964-9_29Licence: In Copyright

Cite this

Usbeck, R., Ngomo, A. C. N., Röder, M., Gerber, D., Coelho, S. A., Auer, S., & Both, A. (2014). AGDISTIS - Graph-based disambiguation of named entities using linked data. In T. Tudorache, C. Knoblock, P. Groth, C. Goble, C. Welty, A. Bernstein, P. Mika, D. Vrandečić, N. Noy, & K. Janowicz (Eds.), The SemanticWeb - ISWC 2014 - 13th International SemanticWeb Conference, Proceedings (pp. 457-471). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8796). Springer-Verlag Wien. https://doi.org/10.1007/978-3-319-11964-9_29

Usbeck, Ricardo ; Ngomo, Axel Cyrille Ngonga ; Röder, Michael et al. / AGDISTIS - Graph-based disambiguation of named entities using linked data. The SemanticWeb - ISWC 2014 - 13th International SemanticWeb Conference, Proceedings. editor / Tania Tudorache ; Craig Knoblock ; Paul Groth ; Carole Goble ; Chris Welty ; Abraham Bernstein ; Peter Mika ; Denny Vrandečić ; Natasha Noy ; Krzysztof Janowicz. Springer-Verlag Wien, 2014. pp. 457-471 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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 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.",

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Usbeck, R, Ngomo, ACN, Röder, M, Gerber, D, Coelho, SA, Auer, S & Both, A 2014, AGDISTIS - Graph-based disambiguation of named entities using linked data. in T Tudorache, C Knoblock, P Groth, C Goble, C Welty, A Bernstein, P Mika, D Vrandečić, N Noy & K Janowicz (eds), The SemanticWeb - ISWC 2014 - 13th International SemanticWeb Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8796, Springer-Verlag Wien, pp. 457-471, 13th International Semantic Web Conference, ISWC 2014, Riva del Garda, Italy, 19.10.14. https://doi.org/10.1007/978-3-319-11964-9_29

AGDISTIS - Graph-based disambiguation of named entities using linked data. / Usbeck, Ricardo; Ngomo, Axel Cyrille Ngonga; Röder, Michael et al.
The SemanticWeb - ISWC 2014 - 13th International SemanticWeb Conference, Proceedings. ed. / Tania Tudorache; Craig Knoblock; Paul Groth; Carole Goble; Chris Welty; Abraham Bernstein; Peter Mika; Denny Vrandečić; Natasha Noy; Krzysztof Janowicz. Springer-Verlag Wien, 2014. p. 457-471 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8796).

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

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

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

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

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A2 - Knoblock, Craig

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A2 - Bernstein, Abraham

A2 - Mika, Peter

A2 - Vrandečić, Denny

A2 - Noy, Natasha

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PB - Springer-Verlag Wien

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ER -

Usbeck R, Ngomo ACN, Röder M, Gerber D, Coelho SA, Auer S et al. AGDISTIS - Graph-based disambiguation of named entities using linked data. In Tudorache T, Knoblock C, Groth P, Goble C, Welty C, Bernstein A, Mika P, Vrandečić D, Noy N, Janowicz K, editors, The SemanticWeb - ISWC 2014 - 13th International SemanticWeb Conference, Proceedings. Springer-Verlag Wien. 2014. p. 457-471. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-11964-9_29

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

Abstract

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Research areas and keywords

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