Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Anh Tuan Luu, Haiteng Zhao, Qika Lin, Jun Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Qiushi Sun, Kanzhi Cheng, Junxian He, Jun Liu, Zhiyong Wu. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Traditional knowledge graph (KG) embedding methods aim to represent entities and relations in a low-dimensional space, primarily focusing on static graphs. However, real-world KGs are dynamically evolving with the constant addition of entities, relations and facts. To address such dynamic nature of KGs, several continual knowledge graph embedding (CKGE) methods have been developed to efficiently update KG embeddings to accommodate new facts while maintaining learned knowledge. As KGs grow at different rates and scales in real-world scenarios, existing CKGE methods often fail to consider the varying scales of updates and lack systematic evaluation throughout the entire update process. In this paper, we propose SAGE, a scale-aware gradual evolution framework for CKGE. Specifically, SAGE firstly determine the embedding dimensions based on the update scales and expand the embedding space accordingly. The Dynamic Distillation mechanism is further employed to balance the preservation of learned knowledge and the incorporation of new facts. We conduct extensive experiments on seven benchmarks, and the results show that SAGE consistently outperforms existing baselines, with a notable improvement of 1.38% in MRR, 1.25% in H@1 and 1.6% in H@10. Furthermore, experiments comparing with fixed dimensions methods show that SAGE achieves optimal performance on every snapshot, demonstrating the importance of adaptive embedding dimensions in CKGE.

Relation extraction (RE) aims to identify the relation between two entities within a sentence, which plays a crucial role in information extraction. Traditional supervised setting on RE does not fit the actual scenario, due to the continuous emergence of new relations and the unavailability of massive labeled examples. Continual few-shot relation extraction (CFS-RE) is proposed as a potential solution to the above situation, which requires the model to learn new relations sequentially from a few examples. Apparently, CFS-RE is more challenging than previous RE, as the catastrophic forgetting of old knowledge and few-shot overfitting on a handful of examples. To this end, we propose a novel flexible-prompt framework on pretrained language model named FPrompt-PLM for CFS-RE, which includes flexible-prompt embedding, pretrained-language understanding, and nearest-prototype learning modules. Note that two pools in FPrompt-PLM, i.e., prompt and prototype pools, are continual updated and applied for prediction of all seen relations at current time-step. The former pool records the distinctive prompt embedding in each time period, and the latter records all learned relation prototypes. Besides, three progressive stages are introduced to learn FPrompt-PLM's parameters and apply this model for CFS-RE testing, which includes meta-training, continual meta-finetuning, and testing stages. And we improve the CFS-RE loss by incorporating multiple distillation losses as well as a novel prototype-diversity loss in these stages to alleviate the catastrophic forgetting and few-shot overfitting problems. Comprehensive experiments on two widely-used datasets show that FPrompt-PLM achieves significant performance improvements over the SOTA baselines.