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2021 | OriginalPaper | Chapter

Data Augmentation for Fairness in Personal Knowledge Base Population

Authors : Lingraj S. Vannur, Balaji Ganesan, Lokesh Nagalapatti, Hima Patel, M. N. Tippeswamy

Published in: Trends and Applications in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

Cold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents. While neural networks have led to improvements in the different tasks that are part of KBP, the overall F1 of the end-to-end system remains quite low. This problem is more acute in personal knowledge bases, which present additional challenges with regard to data protection, fairness and privacy. In this work, we use data augmentation to populate a more complete personal knowledge base from the TACRED dataset. We then use explainability techniques and representative set sampling to show that the augmented knowledge base is more fair and diverse as well.

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Appendix
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Metadata
Title
Data Augmentation for Fairness in Personal Knowledge Base Population
Authors
Lingraj S. Vannur
Balaji Ganesan
Lokesh Nagalapatti
Hima Patel
M. N. Tippeswamy
Copyright Year
2021
Book
Trends and Applications in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-75014-5

Electronic ISBN: 978-3-030-75015-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-75015-2_15

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