2024年7月15日，Elsevier 旗下top期刊《Knowledge-Based Systems》在线发表了云南师范大学民族教育信息化教育部重点实验室甘健侯教授团队的最新研究成果《TracKGE: Transformer with Relation-pattern Adaptive Contrastive Learning for Knowledge Graph Embedding》。云南师范大学为第一作者兼通讯作者单位。云南师范大学民族教育信息化教育部重点实验室甘健侯教授、周菊香教授为通讯作者。

https://www.sciencedirect.com/science/article/abs/pii/S0950705124008529

Abstract

Knowledge Graphs, fundamental to intelligent applications, are increasingly critical in various domains, enhancing tasks like precise searching and personalized recommendation. Effectively representing entities and relationships in these graphs is key, especially as the Transformer model, despite its representational prowess, faces challenges in adapting to the graph’s structure and complex relations. In this work, we present the Transformer with Relation-pattern Adaptive Contrastive Learning for Knowledge Graph Embedding (TracKGE). Specifically, TracKGE transforms the structural information of the knowledge graph into a sequence format that is more manageable for Transformers. In addition, we employ a relation-pattern adaptive contrastive learning module to capture a richer semantic and complex relationship pattern information of the knowledge graph. Lastly, by introducing a mask node model, it addresses the issue of incomplete information in the knowledge graph, further enhancing the model’s capability to capture implicit relationships within it. To evaluate the performance of our model, we have chosen well-established models as baselines and executed link prediction tasks on four renowned datasets. Our experimental results reveal that our model excels in representing the semantics and intricate structures of Knowledge Graphs. It outperforms other advanced baseline models, showcasing its superior capability in handling complex data representations.

扩展阅读：

云师大信息学院甘健侯教授课题组在国际知名TOP期刊《人工智能的工程应用》上发表研究成果

云南师范大学甘健侯教授

甘健侯-云南师范大学-教育学部