Kexuan Xin — accepted AI-conference papers

2026

Breaking the Reasoning Horizon in Entity Alignment Foundation Models

Yuanning Cui, Zequn Sun, Wei Hu, Kexuan Xin, Zhangjie Fu

IJCAI 2026

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.

Cited by 0 Scholar ↗

2022

Ensemble Semi-supervised Entity Alignment via Cycle-Teaching

Kexuan Xin, Zequn Sun, Wen Hua, Bing Liu, Wei Hu, Jianfeng Qu +1

AAAI 2022technical

Entity alignment is to find identical entities in different knowledge graphs. Although embedding-based entity alignment has recently achieved remarkable progress, training data insufficiency remains a critical challenge. Conventional semi-supervised methods also suffer from the incorrect entity alig…

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