Towards Linked Open Data Enabled Data Mining - Strategies for Feature Generation, Propositionalization, Selection, and Consolidation
Towards Linked Open Data Enabled Data Mining - Strategies for Feature Generation, Propositionalization, Selection, and Consolidation
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DOI：
10.1007/978-3-319-18818-8_50
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发表时间：
2015-05
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通讯作者：
Petar Ristoski
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文献类型：
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作者：
Petar Ristoski
来自关联开放数据源的背景知识可用于改善手头数据挖掘问题的结果：预测模型可以变得更准确，描述性模型可以揭示更多有趣的发现。然而，收集和整合背景知识是一项繁琐的手工工作。在本文中，我们提出了一套desiderata，并确定开发一个框架，从关联数据的数据挖掘功能的无监督生成的挑战。
Background knowledge from Linked Open Data sources can be used to improve the results of a data mining problem at hand: predictive models can become more accurate, and descriptive models can reveal more interesting findings. However, collecting and integrating background knowledge is a tedious manual work. In this paper we propose a set of desiderata, and identify the challenges for developing a framework for unsupervised generation of data mining features from Linked Data.