Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs

Authors

  • Yuanning Cui Nanjing University
  • Yuxin Wang Nanjing Univerisity
  • Zequn Sun Nanjing University
  • Wenqiang Liu Tencent
  • Yiqiao Jiang Tencent
  • Kexin Han Tencent
  • Wei Hu Nanjing University

DOI:

https://doi.org/10.1609/aaai.v37i4.25539

Keywords:

DMKM: Linked Open Data, Knowledge Graphs & KB Completion

Abstract

Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and transfer new knowledge through growth. Motivated by this, we delve into an expanding field of KG embedding in this paper, i.e., lifelong KG embedding. We consider knowledge transfer and retention of the learning on growing snapshots of a KG without having to learn embeddings from scratch. The proposed model includes a masked KG autoencoder for embedding learning and update, with an embedding transfer strategy to inject the learned knowledge into the new entity and relation embeddings, and an embedding regularization method to avoid catastrophic forgetting. To investigate the impacts of different aspects of KG growth, we construct four datasets to evaluate the performance of lifelong KG embedding. Experimental results show that the proposed model outperforms the state-of-the-art inductive and lifelong embedding baselines.

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Published

2023-06-26

How to Cite

Cui, Y., Wang, Y., Sun, Z., Liu, W., Jiang, Y., Han, K., & Hu, W. (2023). Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 4217-4224. https://doi.org/10.1609/aaai.v37i4.25539

Issue

Section

AAAI Technical Track on Data Mining and Knowledge Management