MMKG: Multi-modal Knowledge Graphs

被引：89

作者：

Liu, Ye ^{[1
]}

Li, Hui ^{[2
]}

Garcia-Duran, Alberto ^{[3
]}

Niepert, Mathias ^{[4
]}

Onoro-Rubio, Daniel ^{[4
]}

Rosenblum, David S. ^{[1
]}

机构：

[1] Natl Univ Singapore, Singapore, Singapore

[2] Xiamen Univ, Xiamen, Fujian, Peoples R China

[3] Ecole Polytech Fed Lausanne, Lausanne, Switzerland

[4] NEC Labs Europe, Heidelberg, Germany

来源：

SEMANTIC WEB, ESWC 2019 | 2019年 / 11503卷

关键词：

D O I：

10.1007/978-3-030-21348-0_30

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present Mmkg, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs. Therefore, multi-relational link prediction and entity matching communities can benefit from this resource. We believe this data set has the potential to facilitate the development of novel multi-modal learning approaches for knowledge graphs. We validate the utility of Mmkg in the sameAs link prediction task with an extensive set of experiments. These experiments show that the task at hand benefits from learning of multiple feature types.

引用

页码：459 / 474

页数：16

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