DL-Learner Structured Machine Learning on Semantic Web Data

Lorenz Bühmann, Jens Lehmann, Patrick Westphal, Simon Bin
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引用次数: 27

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.
基于语义Web数据的DL-Learner结构化机器学习
下面的文章是期刊论文“DL-Learner:语义网上归纳学习的框架”的扩展摘要。在这篇系统论文中,我们描述了DL-Learner框架。它适用于不同标准机器学习场景中的各种数据和模式分析任务,例如生命科学,以及语义Web特定的应用,例如本体学习和丰富。自2007年创建以来,它已成为监督结构化机器学习的主要基于OWL和rdf的软件框架,包括几个算法实现、使用示例以及在框架之上构建的应用程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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