DL-Learner Structured Machine Learning on Semantic Web Data-Reference-Cited by-同舟云学术

DL-Learner Structured Machine Learning on Semantic Web Data

Published:2018 Issue: Volume: Page:
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Container-title:Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18
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Author:

Bühmann Lorenz¹,Lehmann Jens²,Westphal Patrick¹,Bin Simon¹

Affiliation:

1. University of Leipzig, Leipzig, Germany

2. Fraunhofer IAIS & University of Bonn, Sankt Augustin, Germany

Funder

EU Horizon 2020

German Research Foundation

EU FP7

German Ministry for Economic Affairs and Energy

Publisher

ACM Press

Reference22 articles.

1. Pieter Bonte, Femke Ongenae, and Filip De Turck. 2016. Learning Semantic Rules for Intelligent Transport Scheduling in Hospitals. In Proc. of the 5th Workshop on Data Mining and Knowledge Discovery meets Linked Open Data.

2. Lorenz Bühmann, Daniel Fleischhacker, Jens Lehmann, Andre Melo, and Johanna Völker. 2014. Inductive Lexical Learning of Class Expressions. In Knowledge Engineering and Knowledge Management (LNCS), Vol. 8876. Springer International Publishing, 42--53.

3. Lorenz Bühmann and Jens Lehmann. 2012. Universal OWL Axiom Enrichment for Large Knowledge Bases. In Proc. of EKAW 2012. 57--71.

4. Lorenz Bühmann and Jens Lehmann. 2013. Pattern Based Knowledge Base Enrichment. In 12th International Semantic Web Conference, 21--25 October 2013, Sydney, Australia. 33--48.

5. Lorenz Bühmann, Jens Lehmann, and Patrick Westphal. 2016. DL-Learner--A framework for inductive learning on the Semantic Web. Web Semantics: Science, Services and Agents on the World Wide Web 39 (2016), 15--24.

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