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Correlation embedding learning with dynamic semantic enhanced sampling for knowledge graph completion

  • 19-05-2023
Published in:
Authors
Haojie Nie
Xiangguo Zhao
Xin Bi
Yuliang Ma
George Y. Yuan
Published in
World Wide Web | Issue 5/2023

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Abstract

The article introduces a new method for knowledge graph completion that addresses the issues of incomplete knowledge graphs and low-quality negative samples. It presents a dynamic semantic sampling algorithm that ensures high-quality negative samples for training and a correlation embedding model that captures sequential and convolutional correlations within triples. The method is experimentally validated on two benchmark datasets, demonstrating significant improvements in link prediction tasks compared to existing methods. The article highlights the importance of high-quality negative samples and the effectiveness of the proposed correlation embedding model in enhancing knowledge graph representations.
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previous article An empirical study of pre-trained language models in simple knowledge graph question answering
next article Neighboring relation enhanced inductive knowledge graph link prediction via meta-learning
Title
Correlation embedding learning with dynamic semantic enhanced sampling for knowledge graph completion
Authors
Haojie Nie
Xiangguo Zhao
Xin Bi
Yuliang Ma
George Y. Yuan
Publication date
19-05-2023
Publisher
Springer US
Published in
World Wide Web / Issue 5/2023
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-023-01167-x
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