Knowledge Representation Learning: A Quantitative Review

Yankai Lin, Xu Han, Ruobing Xie, Zhiyuan Liu, Maosong Sun
12/28/2018
cs.CL

Official code: thunlp/OpenKE (4,057 stars, last commit 2024-01-10).

Abstract

Knowledge representation learning (KRL) aims to represent entities and relations in knowledge graph in low-dimensional semantic space, which have been widely used in massive knowledge-driven tasks. In this article, we introduce the reader to the motivations for KRL, and overview existing approaches for KRL. Afterwards, we extensively conduct and quantitative comparison and analysis of several typical KRL methods on three evaluation tasks of knowledge acquisition including knowledge graph completion, triple classification, and relation extraction. We also review the real-world applications of KRL, such as language modeling, question answering, information retrieval, and recommender systems. Finally, we discuss the remaining challenges and outlook the future directions for KRL. The codes and datasets used in the experiments can be found in https://github.com/thunlp/OpenKE.

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Code Implementations(1)

thunlp/OpenKEOfficial100%

An Open-Source Package for Knowledge Embedding (KE)

4,057986C, C++Oct 8, 20172 years ago
knowledge-embedding

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Cite this paper

@article{lin2018knowledge,
  title  = {Knowledge Representation Learning: A Quantitative Review},
  author = {Yankai Lin and Xu Han and Ruobing Xie and Zhiyuan Liu and Maosong Sun},
  year   = {2018},
  eprint = {1812.10901},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1812.10901}
}

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