Papers by Linyan Yang
DAEA: Enhancing Entity Alignment in Real-World Knowledge Graphs Through Multi-Source Domain Adaptation (2025.coling-main)
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| Challenge: | Entity Alignment (EA) is a critical task in Knowledge Graph (KG) integration. |
| Approach: | They propose a novel approach that leverages the data characteristics of synthetic benchmarks to improve performance in real-world datasets. |
| Outcome: | The proposed approach outperforms state-of-the-art models on real-world datasets and achieves a 29.94% improvement in Hits@1 on DOREMUS and 5.64% improvement on AGROLD. |
EasyEA: Large Language Model is All You Need in Entity Alignment Between Knowledge Graphs (2025.findings-acl)
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| Challenge: | Entity alignment (EA) aims to identify entities in different knowledge graphs (KGs) that represent the same real-world object. |
| Approach: | They propose an end-to-end EA framework based on large language models that requires no training to implement. |
| Outcome: | The proposed framework significantly reduces the reliance on seed entity pairs while achieving state-of-the-art (SOTA) performance on diverse datasets. |
Advancing Cross-Lingual Entity Alignment with Large Language Models: Tailored Sample Segmentation and Zero-Shot Prompts (2024.findings-emnlp)
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| Challenge: | Existing approaches to integrate large language models into cross-lingual entity alignment tasks pose challenges in handling large-scale data, generating suitable data samples, and adapting prompts for the EA task. |
| Approach: | They propose a framework that integrates distance feature extraction, sample **Seg**mentation, and zero-shot prompts to integrate LLMs into cross-lingual entity alignment tasks. |
| Outcome: | The proposed framework is able to extract features from large-scale data and adapt prompts to the task. |