DL-Learner Structured Machine Learning on Semantic Web Data

Lorenz Bühmann; Jens Lehmann; Patrick Westphal; Simon Bin

doi:10.1145/3184558.3186235

DL-Learner Structured Machine Learning on Semantic Web Data

Lorenz Bühmann
, Jens Lehmann
, Patrick Westphal
, Simon Bin

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

33 Zitate (Scopus)

Abstract

The following paper is an extended summary of the journal paper "DL-Learner A framework for inductive learning on the Semantic Web". In this system paper, we describe the DL-Learner framework. It is beneficial in various data and schema analytic tasks with applications in different standard machine learning scenarios, e.g. life sciences, as well as Semantic Web specific applications such as ontology learning and enrichment. Since its creation in 2007, it has become the main OWL and RDF-based software framework for supervised structured machine learning and includes several algorithm implementations, usage examples and has applications building on top of the framework.

Originalsprache	Englisch
Titel	The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
Redakteure/-innen	Pierre-Antoine Champin, Fabien Gandon, Lionel Medini
Seitenumfang	5
Erscheinungsort	Canton of Geneva
Herausgeber (Verlag)	Association for Computing Machinery, Inc
Erscheinungsdatum	23.04.2018
Seiten	467-471
ISBN (elektronisch)	978-1-4503-5640-4
DOIs	https://doi.org/10.1145/3184558.3186235
Publikationsstatus	Erschienen - 23.04.2018
Extern publiziert	Ja
Veranstaltung	27th International World Wide Web, WWW 2018: Bridging natural and artificial intelligence worldwide - Universität Lyon, Lyon, Frankreich Dauer: 23.04.2018 → 27.04.2018 https://archives.iw3c2.org/www2018/

Bibliographische Notiz

Publisher Copyright:
© 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.

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Fachgebiete und Schlagwörter

Informatik

ASJC Scopus Sachgebiete

Computernetzwerke und -kommunikation
Software.

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editor = "Pierre-Antoine Champin and Fabien Gandon and Lionel Medini",

booktitle = "The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018",

publisher = "Association for Computing Machinery, Inc",

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Bühmann, L, Lehmann, J, Westphal, P & Bin, S 2018, DL-Learner Structured Machine Learning on Semantic Web Data. in P-A Champin, F Gandon & L Medini (Hrsg.), The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018. Association for Computing Machinery, Inc, Canton of Geneva, S. 467-471, 27th International World Wide Web, WWW 2018, Lyon, Frankreich, 23.04.18. https://doi.org/10.1145/3184558.3186235

DL-Learner Structured Machine Learning on Semantic Web Data. / Bühmann, Lorenz; Lehmann, Jens; Westphal, Patrick et al.
The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018. Hrsg. / Pierre-Antoine Champin; Fabien Gandon; Lionel Medini. Canton of Geneva: Association for Computing Machinery, Inc, 2018. S. 467-471.

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

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N2 - The following paper is an extended summary of the journal paper "DL-Learner A framework for inductive learning on the Semantic Web". In this system paper, we describe the DL-Learner framework. It is beneficial in various data and schema analytic tasks with applications in different standard machine learning scenarios, e.g. life sciences, as well as Semantic Web specific applications such as ontology learning and enrichment. Since its creation in 2007, it has become the main OWL and RDF-based software framework for supervised structured machine learning and includes several algorithm implementations, usage examples and has applications building on top of the framework.

AB - The following paper is an extended summary of the journal paper "DL-Learner A framework for inductive learning on the Semantic Web". In this system paper, we describe the DL-Learner framework. It is beneficial in various data and schema analytic tasks with applications in different standard machine learning scenarios, e.g. life sciences, as well as Semantic Web specific applications such as ontology learning and enrichment. Since its creation in 2007, it has become the main OWL and RDF-based software framework for supervised structured machine learning and includes several algorithm implementations, usage examples and has applications building on top of the framework.

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KW - owl

KW - rdf

KW - semantic web

KW - supervised learning

KW - system description

KW - Informatics

UR - https://www.scopus.com/pages/publications/85060818261

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M3 - Article in conference proceedings

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BT - The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018

A2 - Champin, Pierre-Antoine

A2 - Gandon, Fabien

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CY - Canton of Geneva

T2 - 27th International World Wide Web, WWW 2018

Y2 - 23 April 2018 through 27 April 2018

ER -

DL-Learner Structured Machine Learning on Semantic Web Data

Abstract

Bibliographische Notiz

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ASJC Scopus Sachgebiete

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