# CYCLE: Cross-Year Contrastive Learning in Entity-Linking

arXiv: https://arxiv.org/abs/2410.09127 · HTML: https://mlsanity.com/paper/2410.09127/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 36 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 21 of 21)

- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](https://mlsanity.com/paper/1911.06136/) (2019)
- [Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning](https://mlsanity.com/paper/2105.09111/) (2021)
- [Zero-Shot Entity Linking by Reading Entity Descriptions](https://mlsanity.com/paper/1906.07348/) (2019)
- [TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models](https://mlsanity.com/paper/2204.14211/) (2022)
- [An Empirical Study of Graph Contrastive Learning](https://mlsanity.com/paper/2109.01116/) (2021)
- [Temporal Knowledge Graph Completion: A Survey](https://mlsanity.com/paper/2201.08236/) (2022)
- [Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking](https://mlsanity.com/paper/2003.05473/) (2020)
- [Knowledge-Rich Self-Supervision for Biomedical Entity Linking](https://mlsanity.com/paper/2112.07887/) (2021)
- [GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models](https://mlsanity.com/paper/2310.07793/) (2023)
- [Uncovering the Structural Fairness in Graph Contrastive Learning](https://mlsanity.com/paper/2210.03011/) (2022)
- [Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion](https://mlsanity.com/paper/1909.11359/) (2019)
- [CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata](https://mlsanity.com/paper/2101.09969/) (2021)
- [TempEL: Linking Dynamically Evolving and Newly Emerging Entities](https://mlsanity.com/paper/2302.02500/) (2023)
- [Zero-shot Entity Linking with Efficient Long Range Sequence Modeling](https://mlsanity.com/paper/2010.06065/) (2020)
- [Time-aware Path Reasoning on Knowledge Graph for Recommendation](https://mlsanity.com/paper/2108.02634/) (2021)
- [SpEL: Structured Prediction for Entity Linking](https://mlsanity.com/paper/2310.14684/) (2023)
- [TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs](https://mlsanity.com/paper/2210.06281/) (2022)
- [Rethinking and Simplifying Bootstrapped Graph Latents](https://mlsanity.com/paper/2312.02619/) (2023)
- [End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings](https://mlsanity.com/paper/2002.11143/) (2020)
- [Wasserstein Adversarial Learning based Temporal Knowledge Graph Embedding](https://mlsanity.com/paper/2205.01873/) (2022)

## Abstract (arXiv)

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 CYCLE: Cross-Year Contrastive Learning for Entity-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 textitpositive samples and newly removed ones as textitnegative 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 urlhttps://github.com/pengyu-zhang/CYCLE-Cross-Year-Contrastive-Learning-in-Entity-Linking.