Knowledge graph embeddings (KGE) are effective for representing factual data for numerous applications. However, real-world facts continually evolve, necessitating ongoing updates to knowledge graphs as new information emerges. Under these circumstances, existing KGE models in transductive, inductive, and continual learning settings are prone to catastrophic forgetting or require costly retraining to integrate new information. To address these challenges, we propose a novel model called the Context-aware Adaptive learning model for Knowledge Graph Embeddings (CAKGE). Our model first identifies semantic-relevant entities and uncovers latent relational paths to facilitate the acquisition of new knowledge. To ensure the paths are semantically aligned with the query, we employ a context-aware fusion module, which leverages multiple specialized expert networks to assess and integrate the relevance of these relational paths. Building on this, we introduce an adaptive message aggregation module that incorporates a knowledge replay strategy, enabling the model to integrate both new and existing knowledge efficiently, without retraining the knowledge graph. Additionally, to mitigate catastrophic forgetting, we reformulate the challenge of aligning new with existing knowledge as a graph-matching task using the Fused Gromov-Wasserstein distance, enabling the alignment of old and new knowledge from both semantic and topological perspectives. Furthermore, we provide theoretical guarantees for the expressiveness and reasoning ability of CAKGE, showing that it is the first unified framework tackling transductive, inductive, and continual settings. Extensive experiments show that CAKGE achieves state-of-the-art performance, demonstrating its effectiveness in dynamic KGE modeling.
Cao et al. (2026) studied this question.