A Collection of Benchmark Datasets for Systematic Evaluations of Machine Learning on the Semantic Web

被引：56

作者：

Ristoski, Petar ^{[1
]}

de Vries, Gerben Klaas Dirk ^{[2
]}

Paulheim, Heiko ^{[1
]}

机构：

[1] Univ Mannheim, Res Grp Data & Web Sci, Mannheim, Germany

[2] WizeNoze, Amsterdam, Netherlands

来源：

SEMANTIC WEB - ISWC 2016, PT II | 2016年 / 9982卷

关键词：

Linked Open Data; Machine learning; Datasets; Benchmarking;

D O I：

10.1007/978-3-319-46547-0_20

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the recent years, several approaches for machine learning on the Semantic Web have been proposed. However, no extensive comparisons between those approaches have been undertaken, in particular due to a lack of publicly available, acknowledged benchmark datasets. In this paper, we present a collection of 22 benchmark datasets of different sizes. Such a collection of datasets can be used to conduct quantitative performance testing and systematic comparisons of approaches.

引用

页码：186 / 194

页数：9

共 17 条

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