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STCKGE: Continual knowledge graph embedding based on spatial transformation
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AI
DOI：10.1016/j.knosys.2025.114337.png)
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Abstract
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• Proposes STCKGE, a novel spatial transformation-based CKGE framework featuring dual-component entity representations (base vector + offset vector) and relation-aware spatial regions, significantly enhancing complex relational modeling (e.g., multi-hop) while reducing new knowledge dependency on historical embeddings. • Introduces a Bidirectional Collaborative Update (BCU) strategy that efficiently propagates knowledge through lightweight offset vector operations, minimizing retraining costs for both new and historical knowledge. • Experimental results confirm STCKGE’s strong performance in multi-hop relationship learning and prediction accuracy, with an average MRR improvement of 5.4 %. • Constructs and releases the MULTI benchmark dataset with explicit multi-hop facts, addressing selection bias in existing CKGE benchmarks.
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