Global RDF Vector Space Embeddings

被引：39

作者：

Cochez, Michael ^{[1
,2
,3
]}

Ristoski, Petar ^{[4
]}

Ponzetto, Simone Paolo ^{[4
]}

Paulheim, Heiko ^{[4
]}

机构：

[1] Fraunhofer FIT, D-53754 St Augustin, Germany

[2] Rhein Westfal TH Aachen, Informat 5, Aachen, Germany

[3] Univ Jyvaskyla, Fac Informat Technol, Jyvaskyla, Finland

[4] Univ Mannheim, Data & Web Sci Grp, Mannheim, Germany

来源：

SEMANTIC WEB - ISWC 2017, PT I | 2017年 / 10587卷

关键词：

Graph embeddings; Linked open data; Data mining;

D O I：

10.1007/978-3-319-68288-4_12

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Vector space embeddings have been shown to perform well when using RDF data in data mining and machine learning tasks. Existing approaches, such as RDF2Vec, use local information, i.e., they rely on local sequences generated for nodes in the RDF graph. For word embeddings, global techniques, such as GloVe, have been proposed as an alternative. In this paper, we show how the idea of global embeddings can be transferred to RDF embeddings, and show that the results are competitive with traditional local techniques like RDF2Vec.

引用

页码：190 / 207

页数：18

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