DL-Learner-A framework for inductive learning on the Semantic Web

被引：68

作者：

Buehmann, Lorenz ^{[1
]}

Lehmann, Jens ^{[2
]}

Westphal, Patrick ^{[1
]}

机构：

[1] Univ Leipzig, Inst Comp Sci, AKSW Grp, Augustuspl 10, D-04009 Leipzig, Germany

[2] Univ Bonn, Inst Comp Sci, Romerstr 164, D-53117 Bonn, Germany

来源：

JOURNAL OF WEB SEMANTICS | 2016年 / 39卷

关键词：

System description; Machine learning; Supervised learning; Semantic Web; OWL; RDF; PREDICTIVE TOXICOLOGY CHALLENGE;

D O I：

10.1016/j.websem.2016.06.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this system paper, wedescribe the DL-Learner framework, which supports supervised machine learning using OWL and RDF for background knowledge representation. It can be beneficial in various data and schema analysis tasks with applications in different standard machine learning scenarios, e.g. in the 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. The article gives an overview of the framework with a focus on algorithms and use cases. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：15 / 24

页数：10

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