Name disambiguation has long been a significant issue in many fields, such as literature management and social analysis. In recent years, methods based on graph networks have performed well in name disambiguation, but these works have rarely used heterogeneous graphs to capture relationships between nodes. Heterogeneous graphs can extract more comprehensive relationship information so that more accurate node embedding can be learned. Therefore, a Dual-Channel Heterogeneous Graph Network is proposed to solve the name disambiguation problem. We use the heterogeneous graph network to capture various node information to ensure that our method can learn more accurate data structure information. In addition, we use fastText to extract the semantic information of the data. Then, a clustering method based on DBSCAN is used to classify academic papers by different authors into different clusters. In many experiments based on real datasets, our method achieved high accuracy, which proves its effectiveness.

Knowledge graphs change over time, for example, when new entities are introduced or entity descriptions change. This impacts the performance of entity linking, a key task in many uses of knowledge graphs such as web search and recommendation. Specifically, entity linking models exhibit temporal degradation - their performance decreases the further a knowledge graph moves from its original state on which an entity linking model was trained. To tackle this challenge, we introduce TIGER: a Temporally Improved Graph Entity Linker. By incorporating structural information between entities into the model, we enhance the learned representation, making entities more distinguishable over time. The core idea is to integrate graph-based information into text-based information, from which both distinct and shared embeddings are based on an entity’s feature and structural relationships and their interaction. Experiments on three datasets show that our model can effectively prevent temporal degradation, demonstrating a 16.24% performance boost over the state-of-the-art in a temporal setting when the time gap is one year and an improvement to 20.93% as the gap expands to three years. The code and data are made available at https://github.com/pengyu-zhang/TIGER-Temporally-Improved-Graph-Entity-Linker.

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 positive samples and newly removed ones as 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 https://github.com/pengyu-zhang/CYCLE-Cross-Year-Contrastive-Learning-in-Entity-Linking