@inproceedings{kocijan-lukasiewicz-2021-knowledge,
title = "Knowledge Base Completion Meets Transfer Learning",
author = "Kocijan, Vid and
Lukasiewicz, Thomas",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.524",
doi = "10.18653/v1/2021.emnlp-main.524",
pages = "6521--6533",
abstract = "The aim of knowledge base completion is to predict unseen facts from existing facts in knowledge bases. In this work, we introduce the first approach for transfer of knowledge from one collection of facts to another without the need for entity or relation matching. The method works for both canonicalized knowledge bases and uncanonicalized or open knowledge bases, i.e., knowledge bases where more than one copy of a real-world entity or relation may exist. Such knowledge bases are a natural output of automated information extraction tools that extract structured data from unstructured text. Our main contribution is a method that can make use of a large-scale pretraining on facts, collected from unstructured text, to improve predictions on structured data from a specific domain. The introduced method is the most impactful on small datasets such as ReVerb20K, where we obtained a 6{\%} absolute increase of mean reciprocal rank and 65{\%} relative decrease of mean rank over the previously best method, despite not relying on large pre-trained models like BERT.",
}
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<abstract>The aim of knowledge base completion is to predict unseen facts from existing facts in knowledge bases. In this work, we introduce the first approach for transfer of knowledge from one collection of facts to another without the need for entity or relation matching. The method works for both canonicalized knowledge bases and uncanonicalized or open knowledge bases, i.e., knowledge bases where more than one copy of a real-world entity or relation may exist. Such knowledge bases are a natural output of automated information extraction tools that extract structured data from unstructured text. Our main contribution is a method that can make use of a large-scale pretraining on facts, collected from unstructured text, to improve predictions on structured data from a specific domain. The introduced method is the most impactful on small datasets such as ReVerb20K, where we obtained a 6% absolute increase of mean reciprocal rank and 65% relative decrease of mean rank over the previously best method, despite not relying on large pre-trained models like BERT.</abstract>
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%0 Conference Proceedings
%T Knowledge Base Completion Meets Transfer Learning
%A Kocijan, Vid
%A Lukasiewicz, Thomas
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F kocijan-lukasiewicz-2021-knowledge
%X The aim of knowledge base completion is to predict unseen facts from existing facts in knowledge bases. In this work, we introduce the first approach for transfer of knowledge from one collection of facts to another without the need for entity or relation matching. The method works for both canonicalized knowledge bases and uncanonicalized or open knowledge bases, i.e., knowledge bases where more than one copy of a real-world entity or relation may exist. Such knowledge bases are a natural output of automated information extraction tools that extract structured data from unstructured text. Our main contribution is a method that can make use of a large-scale pretraining on facts, collected from unstructured text, to improve predictions on structured data from a specific domain. The introduced method is the most impactful on small datasets such as ReVerb20K, where we obtained a 6% absolute increase of mean reciprocal rank and 65% relative decrease of mean rank over the previously best method, despite not relying on large pre-trained models like BERT.
%R 10.18653/v1/2021.emnlp-main.524
%U https://aclanthology.org/2021.emnlp-main.524
%U https://doi.org/10.18653/v1/2021.emnlp-main.524
%P 6521-6533
Markdown (Informal)
[Knowledge Base Completion Meets Transfer Learning](https://aclanthology.org/2021.emnlp-main.524) (Kocijan & Lukasiewicz, EMNLP 2021)
ACL
- Vid Kocijan and Thomas Lukasiewicz. 2021. Knowledge Base Completion Meets Transfer Learning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6521–6533, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.