Article

RDF2Vec: RDF Graph Embeddings for Data Mining

Authors:

Petar Ristoski,

Heiko PaulheimAuthors Info & Claims

The Semantic Web – ISWC 2016: 15th International Semantic Web Conference, Kobe, Japan, October 17–21, 2016, Proceedings, Part I

Pages 498 - 514

https://doi.org/10.1007/978-3-319-46523-4_30

Published: 17 October 2016 Publication History

Abstract

Linked Open Data has been recognized as a valuable source for background information in data mining. However, most data mining tools require features in propositional form, i.e., a vector of nominal or numerical features associated with an instance, while Linked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs. We generate sequences by leveraging local information from graph sub-structures, harvested by Weisfeiler-Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs. Our evaluation shows that such vector representations outperform existing techniques for the propositionalization of RDF graphs on a variety of different predictive machine learning tasks, and that feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.

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Cited By

Böckling MPaulheim HDetzler S(2024)Comparing Spatial-Temporal Knowledge Graph on Spatial Downstream TasksProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691321(581-584)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691321
Schwabe TAcosta M(2024)Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural NetworksProceedings of the ACM on Management of Data10.1145/36392992:1(1-26)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639299
Sultana THossain MMorshed MAfridi TLee Y(2024)Inductive autoencoder for efficiently compressing RDF graphsInformation Sciences: an International Journal10.1016/j.ins.2024.120210662:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120210
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Index Terms

RDF2Vec: RDF Graph Embeddings for Data Mining
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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cover image Guide Proceedings

The Semantic Web – ISWC 2016: 15th International Semantic Web Conference, Kobe, Japan, October 17–21, 2016, Proceedings, Part I

Oct 2016

665 pages

ISBN:978-3-319-46522-7

DOI:10.1007/978-3-319-46523-4

Editors:
Paul Groth
Elsevier Labs, Amsterdam, The Netherlands
,
Elena Simperl
University of Southampton , Southampton, United Kingdom
,
Alasdair Gray
Heriot-Watt University , Edinburgh, United Kingdom
,
Marta Sabou
Vienna University of Technology , Vienna, Austria
,
Markus Krötzsch
Technische Universität Dresden , Dresden, Germany
,
Freddy Lecue
Accenture Technology Labs, Dublin, Ireland and Inria, Sophia Antipolis, France
,
Fabian Flöck
GESIS-Leibniz Institute for the Social Sciences, Cologne, Germany
,
Yolanda Gil
University of Southern California , Marina del Rey, California, USA

© Springer International Publishing AG 2016.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 17 October 2016

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Cited By

Böckling MPaulheim HDetzler S(2024)Comparing Spatial-Temporal Knowledge Graph on Spatial Downstream TasksProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691321(581-584)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691321
Schwabe TAcosta M(2024)Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural NetworksProceedings of the ACM on Management of Data10.1145/36392992:1(1-26)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639299
Sultana THossain MMorshed MAfridi TLee Y(2024)Inductive autoencoder for efficiently compressing RDF graphsInformation Sciences: an International Journal10.1016/j.ins.2024.120210662:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120210
Wang WEberhardt WBromuri S(2024)Personalizing Communication and Segmentation with Random Forest Node EmbeddingExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124621255:PCOnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124621
Mussa ORana OGoossens BOrozco-terWengel PPerera C(2024)Towards Enhancing Linked Data Retrieval in Conversational UIs Using Large Language ModelsWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0573-6_18(246-261)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1007/978-981-96-0573-6_18
Shaban APaulheim H(2024)SnapE – Training Snapshot Ensembles of Link Prediction ModelsThe Semantic Web – ISWC 202410.1007/978-3-031-77844-5_1(3-22)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1007/978-3-031-77844-5_1
Llugiqi MEkaputra FSabou M(2024)Enhancing Machine Learning Predictions Through Knowledge Graph EmbeddingsNeural-Symbolic Learning and Reasoning10.1007/978-3-031-71167-1_15(279-295)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-71167-1_15
Hubert NPaulheim HBrun AMonticolo D(2024)Do Similar Entities Have Similar Embeddings?The Semantic Web10.1007/978-3-031-60626-7_1(3-21)Online publication date: 26-May-2024
https://dl.acm.org/doi/10.1007/978-3-031-60626-7_1
Wang DChen YOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Calibrate and boost logical expressiveness of GNN over multi-relational and temporal graphsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669034(66692-66726)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669034
Verkijk SRoothaert RPernisch RSchlobach S(2023)Do you catch my drift? On the usage of embedding methods to measure concept shift in knowledge graphsProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627555(70-74)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3587259.3627555
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