In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering. However, existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them still far from satisfactory. To mitigate this problem, we propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations. It is noteworthy that owing to the general and elegant design of scoring functions, our framework can incorporate many famous existing methods as special cases. Moreover, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework. Source codes and data can be found at \url{https://github.com/Wentao-Xu/SEEK}.
Network Science Institute Northeastern University（东北大学网络科学研究所）；U.S. Army Research Lab（美国陆军研究实验室）；Khoury College of Computer Sciences Northeastern University（东北大学库里计算机科学学院）
From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding
Deakin University（迪肯大学）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；University of Southern Queensland（南昆士兰大学）；CNPIEC KEXIN LTD（中技科信有限公司）；Swinburne University of Technology（斯威本科技大学）；Xidian University（西安电子科技大学）
A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings
School of Computer Science \& Statistics Trinity College Dublin Dublin, Ireland 0000-0003-0654-2938；School of Computer Science \& Statistics Trinity College Dublin Dublin, Ireland 0000-0001-6462-3248；School of Computer Science \& Statistics Trinity College Dublin Dublin, Ireland 0000-0003-1090-3548
Hide and Seek in Embedding Space: Geometry-based Steganography and Detection in Large Language Models
UCL Centre for Artificial Intelligence, University College London, UK（伦敦大学学院人工智能中心，大学学院伦敦）；School of Computing and Communications, Lancaster University, UK（兰卡斯特大学计算机与通讯学院）；Department of Informatics, University of Sussex, UK（苏塞克斯大学信息学院）；Centre for Psychedelic Research and Centre for Complexity Science, Imperial College London, UK（伦敦帝国学院迷幻研究与复杂科学中心）；Centre for Eudaimonia and Human Flourishing, University of Oxford, UK（牛津大学幸福与人类繁荣中心）
