CYCLE: Cross-Year Contrastive Learning in Entity-Linking

TL;DR

CYCLE introduces a graph contrastive learning approach that leverages cross-year entity relationships to mitigate temporal degradation in entity linking models, significantly improving performance over time.

Contribution

This paper presents a novel cross-year contrastive learning method for entity linking that effectively reduces temporal performance decline by utilizing evolving graph relationships.

Findings

Achieves up to 17.79% performance improvement over state-of-the-art.

Effectively enhances robustness for low-degree, sparsely connected entities.

Demonstrates significant performance gains as the temporal gap increases.

Abstract

Knowledge graphs constantly evolve with new entities emerging, existing definitions being revised, and entity relationships changing. These changes lead to temporal degradation in entity linking models, characterized as a decline in model performance over time. To address this issue, we propose leveraging graph relationships to aggregate information from neighboring entities across different time periods. This approach enhances the ability to distinguish similar entities over time, thereby minimizing the impact of temporal degradation. We introduce \textbf{CYCLE}: \textbf{C}ross-\textbf{Y}ear \textbf{C}ontrastive \textbf{L}earning for \textbf{E}ntity-Linking. This model employs a novel graph contrastive learning method to tackle temporal performance degradation in entity linking tasks. Our contrastive learning method treats newly added graph relationships as \textit{positive} samples and newly removed ones as \textit{negative} samples. This approach helps our model effectively prevent temporal degradation, achieving a 13.90\% performance improvement over the state-of-the-art from 2023 when the time gap is one year, and a 17.79\% improvement as the gap expands to three years. Further analysis shows that CYCLE is particularly robust for low-degree entities, which are less resistant to temporal degradation due to their sparse connectivity, making them particularly suitable for our method. The code and data are made available at \url{https://github.com/pengyu-zhang/CYCLE-Cross-Year-Contrastive-Learning-in-Entity-Linking}.