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

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

33 Citations (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.

Original language	English
Title of host publication	The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
Editors	Pierre-Antoine Champin, Fabien Gandon, Lionel Medini
Number of pages	5
Place of Publication	Canton of Geneva
Publisher	Association for Computing Machinery, Inc
Publication date	23.04.2018
Pages	467-471
ISBN (Electronic)	978-1-4503-5640-4
DOIs	https://doi.org/10.1145/3184558.3186235
Publication status	Published - 23.04.2018
Externally published	Yes
Event	27th International World Wide Web, WWW 2018: Bridging natural and artificial intelligence worldwide - Universität Lyon, Lyon, France Duration: 23.04.2018 → 27.04.2018 https://archives.iw3c2.org/www2018/

Bibliographical note

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Research areas and keywords

machine learning
owl
rdf
semantic web
supervised learning
system description
Informatics

ASJC Scopus Subject Areas

Computer Networks and Communications
Software

Access to Document

10.1145/3184558.3186235Licence: CC BY

Cite this

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keywords = "machine learning, owl, rdf, semantic web, supervised learning, system description, Informatics",

author = "Lorenz B{\"u}hmann and Jens Lehmann and Patrick Westphal and Simon Bin",

note = "Publisher Copyright: {\textcopyright} 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.; 27th International World Wide Web, WWW 2018 : Bridging natural and artificial intelligence worldwide ; Conference date: 23-04-2018 Through 27-04-2018",

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language = "English",

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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 (eds), The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018. Association for Computing Machinery, Inc, Canton of Geneva, pp. 467-471, 27th International World Wide Web, WWW 2018, Lyon, France, 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. ed. / Pierre-Antoine Champin; Fabien Gandon; Lionel Medini. Canton of Geneva: Association for Computing Machinery, Inc, 2018. p. 467-471.

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

TY - CHAP

T1 - DL-Learner Structured Machine Learning on Semantic Web Data

AU - Bühmann, Lorenz

AU - Lehmann, Jens

AU - Westphal, Patrick

AU - Bin, Simon

PY - 2018/4/23

Y1 - 2018/4/23

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.

KW - machine learning

KW - owl

KW - rdf

KW - semantic web

KW - supervised learning

KW - system description

KW - Informatics

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

U2 - 10.1145/3184558.3186235

DO - 10.1145/3184558.3186235

M3 - Article in conference proceedings

AN - SCOPUS:85060818261

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EP - 471

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

A2 - Champin, Pierre-Antoine

A2 - Gandon, Fabien

A2 - Medini, Lionel

PB - Association for Computing Machinery, Inc

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

Bibliographical note

UN SDGs

Research areas and keywords

ASJC Scopus Subject Areas

Access to Document

Fingerprint

Cite this