Paper Info
Title
DL-Learner Structured Machine Learning on Semantic Web Data.
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.
Year
DOI
Venue
2018
10.1145/3184558.3186235
WWW '18: The Web Conference 2018 Lyon France April, 2018
DocType
ISBN
Citations 
Conference
978-1-4503-5640-4
2
PageRank 
References 
Authors
0.36
10
4
Name
Order
Citations
PageRank
Lorenz Bühmann160331.20
Jens Lehmann25375355.08
Patrick Westphal31327.98
Simon Bin431.39