SARA：應用語義輔助之強化主動學習策略提升知識圖譜於實體對齊之效能

Abstract

本篇論文提出了一個名為 SARA 的框架，它是一種語義輔助的增強式主動學習框架，用於在有限監督場景下增強實體對齊（EA）。SARA 解決了許多現實世界場景中 EA 的挑戰，例如：知識圖譜的異質性以及訓練數據的有限性。SARA 透過結合增強式主動學習與語義訊息，有效地在有限的標記數據中選擇有價值的實體對，並利用提出的 Sentence-BERT based 的成對語言模型來學習能夠捕捉實體名稱語義的實體名稱嵌入。透過將實體名稱嵌入與結構嵌入相結合，並使用一種新穎的語義輔助對齊損失進行模型的訓練。我們在基準數據集和現實世界的數據集上進行了大量的實驗，證明了SARA相對於現有方法的優越性能，特別是在標記數據有限的情況下，表現更為突出。我們還提供了有關微調策略的深入見解，展示了消融研究，並進行了敏感性分析，以從不同面向驗證SARA的有效性。

This paper introduces SARA, a semantic-assisted reinforced active learning framework for enhancing entity alignment (EA) under limited supervision scenarios. SARA addresses the challenges of EA in real-world scenarios, including knowledge graph heterogeneity and limited training ground truth. SARA effectively selects valuable entity pairs with limited labeled data by combining reinforced active learning and semantic information. It utilizes a pair-wise language model based on Sentence-BERT to learn informative name embeddings that capture entity name semantics. These embeddings are combined with structural embeddings and trained using a novel semantic-assisted alignment loss. Extensive experiments on benchmark datasets and a real-world dataset demonstrate the superiority of SARA over existing approaches, particularly in limited labeled data scenarios. The paper also provides insights into fine-tuning strategies, presents ablation studies, and conducts sensitivity analyses to validate the effectiveness of SARA.