AI Insight
This paper introduces a new foundation model for entity alignment that can match entities across different knowledge graphs without requiring retraining for each new dataset. The key innovation is a parallel encoding strategy that uses known entity matches as "anchors" to guide information flow, which addresses the challenge of capturing long-range dependencies in sparse, heterogeneous graph structures. The model incorporates a merged relation graph for global dependencies and demonstrates strong generalization to previously unseen knowledge graphs.
Why it matters
This approach could significantly improve the efficiency of integrating multiple knowledge bases in real-world applications like search engines, recommendation systems, and data integration pipelines. By eliminating the need to retrain models for each new knowledge graph, it reduces computational costs and enables more scalable knowledge graph fusion.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: Entity alignment (EA) is critical for knowledge graph (KG) fusion. Existing EA models lack transferability and are incapable of aligning unseen KGs without retraining. While using graph foundation models (GFMs) offer a solution, we find that directly adapting GFMs to EA remains largely ineffective. This stems from a critical “reasoning horizon gap”: unlike link prediction in GFMs, EA necessitates capturing long-range dependencies across sparse and heterogeneous KG structuresTo address this challenge, we propose a EA foundation model driven by a parallel encoding strategy. We utilize seed EA pairs as local anchors to guide the information flow, initializing and encoding two parallel streams simultaneously. This facilitates anchor-conditioned message passing and significantly shortens the inference trajectory by leveraging local structural proximity instead of global search. Additionally, we incorporate a merged relation graph to model global dependencies and a learnable interaction module for precise matching. Extensive experiments verify the effectiveness of our framework, highlighting its strong generalizability to unseen KGs.
Source: Breaking the Reasoning Horizon in Entity Alignment Foundation Models