Geometry fusion representation for knowledge graph completion using multi-view information bottleneck

Kai Chen, Han Yu, Xin Song, Ye Wang, Yongxue Shan, Aiping Li, Yinxuan Huang, Ziniu Liu

Abstract: The inherent incompleteness of Knowledge Graphs (KGs) has spurred significant research efforts in the domain of knowledge graph completion (KGC), which is grounded in the premise of leveraging existing knowledge to infer the unknown and fill in the gaps. A multitude of studies have focused on representation learning across a spectrum of geometric spaces, namely Euclidean, hyperbolic, and spherical. Each of these spaces excels in modeling distinct structural and characteristic elements within KGs, thereby enhancing the ability to uncover and reason about missing knowledge. Recognizing the distinct modeling advantages of each space, there is a growing effort to integrate these disparate geometries. Despite these efforts, current fusion methods, which rely on simplistic weighted summation, fail to adequately focus on the objectives of reasoning and tend to retain a significant amount of redundant information. To address this, we propose the Multi-View Information Bottleneck based Geometry Fusion (MVIBGF) method for KGC. We utilize a multi-view learning approach, aligning each view with a unique geometric space, and apply the Information Bottleneck principle to enhance mutual information with the inference goal while minimizing it within individual views. This process ensures that only the most salient and reasoning-relevant information is retained, thus enhancing the precision and efficiency of the knowledge integration. Experiments show that MVIBGF outperforms existing KGC methods, proving its robustness and effectiveness in KG modeling.