Orchestrating Plasticity and Stability: A Continual Knowledge Graph Embedding Framework with Bio-Inspired Dual-Mask Mechanism

Ailin Song, Yuhong Chen, Yusong Wang, Shuai Zhong, Mingkun Xu
Proceedings of the 16th Asian Conference on Machine Learning, PMLR 260:1208-1223, 2025.

Abstract

Learning in biological systems involves the intricate modeling of diverse entities and their interrelations, leading to the evolution of logical knowledge networks with accumulating experience. Analogously, knowledge graphs serve as semantic representations of entity relationships, playing a vital role in natural language processing and graph representation learning. However, contemporary knowledge graph embedding models often neglect real-world event updates, while existing continual knowledge graph research predominantly relies on conventional learning methods that inadequately leverage graph structure, thereby compromising their continual learning capabilities. This study introduces a novel Continual Mask Knowledge Graph Embedding framework (CMKGE), designed to address these limitations. CMKGE integrates semantic attributes, network structure, and continual learning mechanisms to capture the dynamic evolution of knowledge. Inspired by biological signal propagation and Dale’s principle, we introduce a dual-mask mechanism for neuronal inhibition and activation. This mechanism automatically filters critical old knowledge, enhancing model plasticity and stability. Through comprehensive evaluations on four datasets, we demonstrate CMKGE’s superiority over state-of-the-art continual embedding models.

Cite this Paper

BibTeX
@InProceedings{pmlr-v260-song25a, title = {{Orchestrating Plasticity and Stability}: {A} Continual Knowledge Graph Embedding Framework with Bio-Inspired Dual-Mask Mechanism}, author = {Song, Ailin and Chen, Yuhong and Wang, Yusong and Zhong, Shuai and Xu, Mingkun}, booktitle = {Proceedings of the 16th Asian Conference on Machine Learning}, pages = {1208--1223}, year = {2025}, editor = {Nguyen, Vu and Lin, Hsuan-Tien}, volume = {260}, series = {Proceedings of Machine Learning Research}, month = {05--08 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v260/main/assets/song25a/song25a.pdf}, url = {https://proceedings.mlr.press/v260/song25a.html}, abstract = {Learning in biological systems involves the intricate modeling of diverse entities and their interrelations, leading to the evolution of logical knowledge networks with accumulating experience. Analogously, knowledge graphs serve as semantic representations of entity relationships, playing a vital role in natural language processing and graph representation learning. However, contemporary knowledge graph embedding models often neglect real-world event updates, while existing continual knowledge graph research predominantly relies on conventional learning methods that inadequately leverage graph structure, thereby compromising their continual learning capabilities. This study introduces a novel Continual Mask Knowledge Graph Embedding framework (CMKGE), designed to address these limitations. CMKGE integrates semantic attributes, network structure, and continual learning mechanisms to capture the dynamic evolution of knowledge. Inspired by biological signal propagation and Dale’s principle, we introduce a dual-mask mechanism for neuronal inhibition and activation. This mechanism automatically filters critical old knowledge, enhancing model plasticity and stability. Through comprehensive evaluations on four datasets, we demonstrate CMKGE’s superiority over state-of-the-art continual embedding models.} }
Endnote
%0 Conference Paper %T Orchestrating Plasticity and Stability: A Continual Knowledge Graph Embedding Framework with Bio-Inspired Dual-Mask Mechanism %A Ailin Song %A Yuhong Chen %A Yusong Wang %A Shuai Zhong %A Mingkun Xu %B Proceedings of the 16th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Vu Nguyen %E Hsuan-Tien Lin %F pmlr-v260-song25a %I PMLR %P 1208--1223 %U https://proceedings.mlr.press/v260/song25a.html %V 260 %X Learning in biological systems involves the intricate modeling of diverse entities and their interrelations, leading to the evolution of logical knowledge networks with accumulating experience. Analogously, knowledge graphs serve as semantic representations of entity relationships, playing a vital role in natural language processing and graph representation learning. However, contemporary knowledge graph embedding models often neglect real-world event updates, while existing continual knowledge graph research predominantly relies on conventional learning methods that inadequately leverage graph structure, thereby compromising their continual learning capabilities. This study introduces a novel Continual Mask Knowledge Graph Embedding framework (CMKGE), designed to address these limitations. CMKGE integrates semantic attributes, network structure, and continual learning mechanisms to capture the dynamic evolution of knowledge. Inspired by biological signal propagation and Dale’s principle, we introduce a dual-mask mechanism for neuronal inhibition and activation. This mechanism automatically filters critical old knowledge, enhancing model plasticity and stability. Through comprehensive evaluations on four datasets, we demonstrate CMKGE’s superiority over state-of-the-art continual embedding models.
APA
Song, A., Chen, Y., Wang, Y., Zhong, S. & Xu, M.. (2025). Orchestrating Plasticity and Stability: A Continual Knowledge Graph Embedding Framework with Bio-Inspired Dual-Mask Mechanism. Proceedings of the 16th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 260:1208-1223 Available from https://proceedings.mlr.press/v260/song25a.html.

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