ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding (2506.08158v1)

Abstract: Continual Knowledge Graph Embedding (CKGE) seeks to integrate new knowledge while preserving past information. However, existing methods struggle with efficiency and scalability due to two key limitations: (1) suboptimal knowledge preservation between snapshots caused by manually designed node/relation importance scores that ignore graph dependencies relevant to the downstream task, and (2) computationally expensive graph traversal for node/relation importance calculation, leading to slow training and high memory overhead. To address these limitations, we introduce ETT-CKGE (Efficient, Task-driven, Tokens for Continual Knowledge Graph Embedding), a novel task-guided CKGE method that leverages efficient task-driven tokens for efficient and effective knowledge transfer between snapshots. Our method introduces a set of learnable tokens that directly capture task-relevant signals, eliminating the need for explicit node scoring or traversal. These tokens serve as consistent and reusable guidance across snapshots, enabling efficient token-masked embedding alignment between snapshots. Importantly, knowledge transfer is achieved through simple matrix operations, significantly reducing training time and memory usage. Extensive experiments across six benchmark datasets demonstrate that ETT-CKGE consistently achieves superior or competitive predictive performance, while substantially improving training efficiency and scalability compared to state-of-the-art CKGE methods. The code is available at: https://github.com/lijingzhu1/ETT-CKGE/tree/main

• The paper introduces a novel task-driven token mechanism to bypass explicit node scoring and reduce computational overhead in continual KG embedding.
• It employs a token-masked embedding alignment mechanism using matrix operations for efficient knowledge transfer across graph snapshots.
• Results from six datasets demonstrate superior predictive performance and significant improvements in training time and memory usage compared to traditional methods.

Efficient Task-driven Tokens for Continual Knowledge Graph Embedding: An Overview

The paper "ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding" addresses pivotal challenges in the domain of Continual Knowledge Graph Embedding (CKGE), which involves the progressive integration of emerging knowledge in dynamic knowledge graphs while preserving previously acquired information. The authors identify two primary limitations in existing CKGE techniques: inefficient knowledge preservation due to arbitrary importance scores for nodes and relations, and the computational burden posed by graph traversal operations needed to determine these scores. In response, they propose ETT-CKGE, a task-oriented approach that leverages efficient tokens to facilitate knowledge transfer without requiring explicit node scoring or exhaustive graph traversal.

Proposed Methodology

ETT-CKGE introduces the concept of learnable tokens, specifically engineered to encapsulate task-relevant signals directly from the CKGE task objective. This design innovation allows the model to bypass traditional node/relation importance estimations and graph traversal operations. The learned tokens operate as consistent guidance across snapshots, enabling the token-masked embedding alignment mechanism between consecutive snapshots of the graph. The idea is to simplify the knowledge transfer process through matrix operations, which significantly optimize training time and memory overhead compared to traditional CKGE methods.

Results and Comparisons

The efficacy of ETT-CKGE is demonstrated through extensive experiments across six standard datasets characterized by different data distributions. Results consistently show that ETT-CKGE achieves either superior or competitive predictive performance relative to leading CKGE methods. Moreover, the training efficiency and scalability are markedly improved, as evidenced by substantial reductions in training times and memory usage.

Implications and Future Research

The outcomes of this paper hold considerable implications for both practical applications and theoretical advancements in CKGE. Practically, ETT-CKGE offers improved applicability in real-world scenarios where large-scale and evolving knowledge graphs are prevalent, such as in recommendation systems, social networks, and biomedical data integration. Theoretically, this task-driven token approach opens new avenues for aligning model architectures directly with task objectives, reducing reliance on heuristic-driven designs. Future research might explore broader applications of learnable tokens within other graph-based tasks and extend the underlying principles to larger, more complex graph structures. Additionally, there is potential to integrate ETT-CKGE with advanced AI architectures, such as LLMs, to further enrich its capabilities in handling vast and dynamic knowledge bases.