用空间变换提升知识图谱持续学习能力，更准地处理复杂关系。
STCKGE:Continual Knowledge Graph Embedding Based on Spatial Transformation
- 通过位置向量与偏移向量联合建模实体，支持高效更新。
- 多跳关系预测平均MRR提升5.4%，优于现有方法。
- 适合需要长期学习复杂关系的场景，如智能问答系统。
当前持续知识图谱嵌入（CKGE）方法主要依赖基于平移的嵌入方式，利用已有知识初始化新事实。尽管常结合微调或持续学习策略以提高效率，但会牺牲预测准确率，且难以支持复杂关系结构（如多跳关系）。为此，我们提出基于空间变换的新型CKGE框架STCKGE。在该框架中，实体位置由基础位置向量与偏移向量共同决定，使模型能更有效地表示复杂关系，并通过简单的空间操作实现对新增与已有知识的高效嵌入更新，无需依赖传统持续学习技术。此外，我们引入双向协同更新策略与均衡嵌入方法，指导参数更新，在显著降低训练成本的同时提升模型精度。我们在七个公开数据集及一个新构建的聚焦多跳关系的数据集MULTI上进行了全面评估。实验结果表明，STCKGE在多跳关系学习和预测准确性方面表现优异，平均MRR提升5.4%。
原文摘要 · Abstract (English)
Current Continual Knowledge Graph Embedding (CKGE) methods primarily rely on translation-based embedding approaches, leveraging previously acquired knowledge to initialize new facts. While these methods often integrate fine-tuning or continual learning strategies to enhance efficiency, they compromise prediction accuracy and lack support for complex relational structures (e.g., multi-hop relations). To address these limitations, we propose STCKGE, a novel CKGE framework based on spatial transformation. In this framework, entity positions are jointly determined by base position vectors and offset vectors, enabling the model to represent complex relations more effectively while supporting efficient embedding updates for both new and existing knowledge through simple spatial operations, without relying on traditional continual learning techniques. Furthermore, we introduce a bidirectional collaborative update strategy and a balanced embedding method to guide parameter updates, effectively minimizing training costs while improving model accuracy. We comprehensively evaluate our model on seven public datasets and a newly constructed dataset (MULTI) focusing on multi-hop relationships. Experimental results confirm STCKGE's strong performance in multi-hop relationship learning and prediction accuracy, with an average MRR improvement of 5.4\%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。